Overview:
This plan outlines a step-by-step strategy for Jeeva AI to build and deploy a suite of custom AI agents for Acme Inc. It is designed to instill confidence in a safe, fast, and secure delivery of the solution, addressing Acme’s specific needs. The plan covers the project scope, phased timeline, system architecture, DevOps, agent designs (with prompt examples), UI/UX, training & support, security/compliance measures, expected business outcomes, and key code/infra details. The goal is to optimize Acme’s operations and data management through agentic AI, in a manner that is technically sound yet easy to understand.
1. Project Scope
Acme requires multiple specialized AI agents to streamline different operational tasks. Below is an overview of each agent’s role, aligned with Acme’s needs as understood from discussions and documents:
● Riddler (Central Intelligence): A secure central AI system that allows authorized employees to query compiled company data and get insightful answers. Riddler can compare information from multiple AI models to identify discrepancies (for example, flagging inconsistencies in cost estimates) and track metrics like subcontractor price creep. In essence, Riddler serves as the “brain” of the platform, enabling advanced Q&A and data analysis across all documents and records.
● File Sweep System (File Sweeper & Chrono Sweeper): An AI-driven file management system for organizing documents (e.g. project transcripts, bids, permits) and automating storage of critical information like bid dates and due dates. File Sweeper will intelligently tag and organize files by content, while Chrono Sweeper will organize and archive files chronologically (ensuring older records or time-sensitive documents are handled appropriately). This brings order to Acme’s unstructured data and ensures important dates (like bid deadlines) are captured and retrievable.
● SIFT Agent (Email Triage): An agent for intelligent email triage that classifies and prioritizes incoming communications. SIFT will read incoming emails and determine their nature (e.g. bid inquiry, internal request, invoice, general query) and urgency level. It tags or sorts emails so that Acme’s team can focus on high-priority and relevant emails first. This helps prevent important messages from being overlooked.
● Bid Centry Agent (Bid Document Management): An agent to automate bid management by processing and organizing bid documents. Bid Centry will ingest bid-related documents (RFPs, bid proposals, etc.), extract key details (client info, project scope, due dates, requirements), and enter them into Acme’s systems or databases. This ensures no bid deadlines are missed and that all proposal requirements are tracked. It reduces manual data entry and speeds up the bidding process.
● Triage Agent (Communication Routing): An agent for prioritizing and routing communications to the appropriate teams. After SIFT classifies an email, the Triage agent decides who or which department should handle it. For example, a bid-related email can be routed directly to the Estimating team, an HR-related email to HR, etc. The Triage agent uses the SIFT classification (and potentially additional analysis of the content) to forward the email or alert the correct personnel promptly. This automation ensures each communication gets to the right team without delay.
● File Sweeper & Chrono Sweeper Agents (Advanced File Management): Agents dedicated to file organization and chronological management. The File Sweeper agent will use AI to categorize and file documents in the appropriate folders or systems (for example, tagging a document as a “Transcript” vs “Contract” and storing accordingly). The Chrono Sweeper will manage data over time – for instance, maintaining a chronological archive of project files or cleaning up/redacting old data after a retention period. Together, these agents (often referred to collectively as the "File Sweep" system) ensure Acme’s files are systematically organized and accessible when needed.
Bid LifeCycle State Management -
● Launch Packages Agent (Project Launch Automation): An agent to automate project initiation workflows. When a new project is kicked off, the Launch Packages agent will generate the “launch package” – e.g. setting up project folders, drafting initial documents or checklists, populating templates with project specifics (names, dates, regulatory steps), and possibly scheduling initial tasks. This agent makes sure that each new project starts with all the necessary paperwork and steps completed, improving consistency and speed in project ramp-up.
● Fang Agent (Fraud Detection): An agent focused on detecting fraud or anomalies in receipts and financial documents. Fang will scan expense reports, invoices, or receipts and flag unusual patterns – for example, duplicate receipts, out-of-range amounts, or mismatches between reported costs and known project budgets. The goal is to catch potential fraud or errors in financial documents automatically, protecting Acme’s finances. This agent can cross-verify data (e.g. comparing a receipt’s details with an internal approved expense list) and alert management if something looks suspicious.
Integration of Agents: These agents are designed to work in concert. For instance, outputs from one agent can feed another – SIFT’s email categorization feeds the Triage agent’s routing decisions, Bid Centry’s extracted data can be used by the Launch Packages agent to populate project templates, and File Sweeper’s organized data is indexed so that Riddler can answer questions using that information. All agents will feed into the Riddler central intelligence hub, which acts as a unified query interface across all data. This modular yet interconnected approach aligns with Acme’s vision of a system-of-systems (a “parent” AI system that can create or manage specialized sub-agents) as discussed in initial meetings. The scope covers delivering each of these agents tailored to Acme’s workflows, using Jeeva’s proven agentic AI platform capabilities.
2. Phased Implementation Timeline
We propose a phased rollout from June to October 2025, with clear milestones to ensure timely delivery and integration. This timeline aligns with Acme’s priorities and allows gradual adoption and testing. Key milestones and deliverables are as follows:
● June 2025 – Project Kickoff and Planning: (By June 10) – Conduct a project kick-off meeting to finalize requirements, assign teams, and set success criteria. Deliverables: a detailed project requirements document and a project plan. Immediately afterward, we begin System Architecture & Agent Scoping, completing the technical design for the overall system and scoping out the core agents (especially the Riddler central system) by mid-late June. By June 24, 2025, we aim to have the complete system architecture diagram, data flow design, and integration points finalized, along with a prototype plan for the initial agents.
● July 2025 – Core Agent Development: Early July – Deliver the first iteration of the AI platform interface and deploy the initial core agents (SIFT, Bid Centry, and Triage) in a test environment. By July 8, 2025, Acme’s team will have access to a working prototype: a secure web interface where they can log in and interact with these core agents. This includes basic email triage functioning on sample or live emails, bid document upload and parsing, and routing of categorized messages. User accounts and role-based logins will be enabled at this stage. Mid-July – Integrate initial Acme’s data into the system and begin validation of agent outputs on real data. By July 22, 2025, the core agents will be processing actual emails and documents in a controlled setting, and we will validate their performance (accuracy of email classification, correctness of bid data extraction, etc.).
● August 2025 – Launch & Training: Early August – Move the core agents into live usage for daily operations and initiate comprehensive training for Acme’s team. By August 5, 2025, the system will be officially in use for critical workflows (email triage fully active on incoming mail, bid parsing on new bids, etc.), under close monitoring. We will conduct on-site or virtual training sessions for different user groups (e.g. project managers, estimators, finance staff) to ensure they are comfortable interacting with the agents. During this period, we will measure the agents’ latency and accuracy against agreed Service Level Agreements (SLAs) (for example, SIFT should categorize an email within 2 seconds, Bid Centry should extract data with 98% field accuracy, etc.). Any issues discovered will be addressed immediately. Deliverables: trained end-users, user feedback collected, and a go-live of core functionalities.
● September 2025 – Feedback Loop and New Agent Development: Throughout September, we enter a workflow refinement and feedback phase. We will actively gather feedback from Acme’s team on the core agents’ performance and the UI/UX. Regular review meetings (weekly or bi-weekly) will be held to discuss what’s working and what needs improvement. Based on actual usage data and user input, we will adjust agent logic or the platform UX (for example, fine-tuning email categorization prompts, adding new categories, improving the interface layout). By mid-September, we will also finalize the design and prioritization for the remaining agents: File Sweeper, Chrono Sweeper, Launch Packages, and Fang. Development of these advanced agents will commence in parallel, following the lessons learned from the core agents. This phase ensures we iterate on the system with Acme’s input before expanding its capabilities.
● October 2025 – Advanced Agent Deployment & Project Conclusion: In October, we focus on delivering the new agents and scaling up the system. By mid-October, the File Sweeper, Chrono Sweeper, Launch Packages, and Fang agents will be developed, tested, and ready for deployment. We will perform a staged rollout of these agents in the production environment – likely one by one or in small batches – to ensure stability. By October 31, 2025, all these advanced agents are expected to be live and fully integrated into the platform. The system will be expanded to cover the full range of Acme’s use cases, and any final tweaks based on user feedback will be applied. We will also conduct a final review and hand-off: deliver complete documentation, architecture diagrams, and provide any final training needed. At project conclusion, we ensure Acme’s team is confident using the system and that it meets the promised performance and security standards. The project will then transition into ongoing support mode as per our agreement.
Each milestone above comes with a deliverable or outcome, ensuring transparency and accountability at every step. This phased approach de-risks the project: Acme’s team will see progress at least every few weeks, rather than waiting months for a big reveal. It also allows us to incorporate feedback continuously. By spacing development from June through October, we align with Acme’s desired timeline and demonstrate the capacity to deliver a complex solution within roughly 5 months (which supports the value of the $250K investment through a clearly managed process).
3. System Architecture
Overview: The system will adopt a modular, secure cloud architecture leveraging Jeeva’s existing tech stack (FastAPI, LangChain, MongoDB, AWS, React). In broad terms, the architecture consists of a web-based user interface, a set of backend services (API and agent logic), integration pipelines for data sources (emails, documents, etc.), external AI model APIs, and storage layers for data and logs. All components are designed with security (encryption, RBAC), performance (scalability, low-latency), and maintainability in mind. Below, we detail the architecture and data flow, followed by specific security measures and an AWS deployment diagram.
3.1 System Components & Data Flow
At a high level, when a user interacts with the system (or when an event like a new email arrives), the flow is as follows:
● User Interface (React Web App): End-users (Acme’s employees) will interact through a secure web application. This UI will allow them to view results (e.g. see triaged emails, query Riddler, upload a bid document, review a flagged receipt) and initiate actions (like submitting a query or confirming an agent’s suggestion). The React app communicates with the backend via HTTPS API calls.
● Backend API (FastAPI Server): The backend is a FastAPI application running in AWS. It exposes RESTful endpoints for the UI and also houses the logic for each agent. For example, endpoints like /email/triage for SIFT, /bid/upload for Bid Centry, /query for Riddler, etc., will be defined. The FastAPI server orchestrates requests to the AI models (via LangChain) and manages data read/write to the database. Each agent’s functionality is encapsulated in modular service components or controllers within this backend. The backend will also include authentication and authorization middleware (likely JWT-based auth for user sessions, ensuring only authorized users/roles can access certain agent endpoints).
● AI Agents & LangChain Orchestration: Within the backend, each agent’s logic is implemented using the LangChain framework (for managing prompts, model calls, and tool usage). For instance:
The SIFT agent uses a LangChain chain that takes an email text and returns a classification (using an LLM).
Riddler might use a LangChain RetrievalQA chain that first retrieves relevant data from the knowledge base (see storage below) and then queries an LLM to answer the question.
Some agents may use LangChain’s tool capabilities – e.g. an OCR tool for Fang (to read image receipts), or a Python tool for Riddler (to perform numeric calculations on data if needed).
Agents can be thought of as “microservices” within the FastAPI app – logically separate, but able to communicate via shared database or internal API calls. This modular design makes it easy to add or update agents without affecting others. It also aligns with Acme’s goal of a system that can be extended with new agents over time.
● External AI Model APIs: Since we are not training our own models, the system will call external AI services for language processing. We plan to use OpenAI’s GPT-O3 (and GPT4.1 where appropriate) as well as potentially Anthropic’s Claude for certain tasks – especially to enable cross-model verification as in Riddler. All calls to these APIs will be made via secure endpoints, and importantly, we will opt out of data retention for model training. (By default, OpenAI’s API does not use submitted data to train models, and we will ensure this is the case – see Security section.) The LangChain library will handle the interface to these models. For tasks like document parsing, we might also leverage specialized AI services: e.g. using AWS Textract for document OCR or form extraction (keeping data within AWS for privacy) and then feeding the extracted text to an LLM for analysis. Each agent will choose the appropriate model or service: GPT-O3 for complex reasoning (Riddler, Launch Packages), GPT4.1 for faster responses where high-level accuracy is sufficient (SIFT classification), and domain-specific tools for things like OCR (Fang’s receipts) or scheduling.
● Data Storage Layers:
MongoDB Database: We will use MongoDB (likely a managed MongoDB Atlas or AWS DocumentDB instance) as the primary database. It will store structured data and metadata: e.g. email metadata and SIFT results (sender, subject, category, priority), extracted fields from bid documents, records of what communications were routed where, user profiles and roles, agent logs, etc. MongoDB’s flexible schema is ideal since different agents produce different data structures. We will also use it to store embeddings for semantic search: e.g. documents and transcripts processed by File Sweeper will have vector embeddings stored (either in a Mongo collection or a vector DB integration) to enable Riddler’s semantic queries. MongoDB will be configured with encryption at rest and secured access (only the app server can query it).
File Storage (AWS S3): Large unstructured files (PDFs of bids, images of receipts, transcripts) will be stored in an encrypted S3 bucket. When Bid Centry or File Sweeper ingest a file, it will first upload to S3 (for persistent storage), then the content will be extracted (text extraction via OCR or text parsing) and the results stored in MongoDB (for quick searching and referencing). The S3 bucket will be private, accessible only to the application (via IAM roles), and all files will be encrypted using AWS-managed keys. Versioning will be enabled so we never lose data if files are updated.
Caching Layer: (If needed) For performance, we may introduce an in-memory cache (like Redis or an AWS ElastiCache service) to store recent query results or frequently accessed data (e.g. results of the latest Riddler queries or a cache of email classifications) to speed up responses. This can also help throttle repetitive requests to the LLM to reduce costs and latency. For the initial scope, caching is an optimization – we’ll implement it if performance tests show benefits.
● Event & Task Pipeline: Not all agents operate purely on user request; some need to react to events or run periodically:
- For email triage (SIFT/Triage), we will set up an email ingestion pipeline. Likely we’ll use an IMAP/SMTP integration or a webhook from Acme’s email server (if using something like Office 365 or Gmail) to notify our system of new emails. A lightweight service or scheduled job will fetch new emails (every minute, for example) and pass them to the SIFT agent for classification. After classification and routing, the system might send out notifications or move the email in the email system via API (e.g. using Microsoft Graph API to label or forward the email to a team’s mailbox).
- For file management (File/Chrono Sweeper), we might schedule a nightly job to do housekeeping (archive old files, send reminders for upcoming due dates found in docs, etc.). Also, when new documents are uploaded (via the UI or email attachments), the File Sweeper agent will automatically categorize and store them.
- For fraud detection (Fang), the agent could run in batch mode (e.g. scan all new receipts at day’s end) or be triggered when a new expense entry is created in whatever system Acme uses. We will integrate with Acme’s finance system or simply provide an interface to upload/check receipts. The key is that Fang will systematically check documents using both rules and AI – e.g. verifying totals, dates, vendor names against an approved list, etc., and log any anomalies.
- Launch Packages might be triggered when a project is marked as “won” or initiated in Acme’s project tracking system. We can integrate via API or even a simple button in the UI to “Launch Project” which when clicked, triggers the agent to compile the package (using data from Bid Centry and templates stored on S3).
All these flows are coordinated via FastAPI and background tasks. We will utilize Python async features or Celery (with an AWS SQS or Redis broker) for any long-running tasks so that the web requests return quickly. For example, uploading a large bid document might initiate an asynchronous task to parse it, with the UI polling for the result or sending an email notification when done.
The diagram below illustrates the high-level architecture in the AWS cloud environment, showing how users, the application, data stores, and external AI services interact:
Diagram: System architecture deployed on AWS. In this architecture, end-users (Acme’s team) access a React frontend (which could be hosted as a static site on S3/CloudFront or served via the FastAPI app). The frontend communicates with the FastAPI backend (running in an AWS ECS container or EC2 instance inside a secure VPC). The backend processes requests using the appropriate agent logic and interacts with the MongoDB database for storing and retrieving data. It also uses an S3 bucket for file storage. For AI computations, the backend calls external LLM services (OpenAI/Anthropic) over the internet – these calls are made securely and do not expose Acme’s data to training (see Security section). The entire system is within Acme’s isolated AWS environment, behind a load balancer and with strict network controls.
This modular architecture ensures that each agent (SIFT, Bid Centry, etc.) operates within the same unified platform but can be scaled or updated independently. If one agent requires more resources (say the email volume increases), we can scale out additional instances of that service without affecting others. Likewise, if a new agent is to be added in the future, it can plug into this architecture without redesigning the whole system.
3.2 Security and Data Handling
Security is paramount in this architecture. We address encryption, access control, data privacy, and auditability as first-class concerns:
● Data Encryption: All data at rest will be encrypted. The MongoDB database will use encryption-at-rest (either via the cloud provider or built-in MongoDB Atlas encryption). The S3 bucket will have server-side encryption (AES-256) enabled by default (with AWS KMS keys). In transit, all communications are over HTTPS/TLS 1.2+, including user access to the web app and backend calls to external APIs. Internally, if the architecture uses a private subnet, we will still enforce TLS for any service-to-service communication as needed. Secrets (like API keys for OpenAI, database credentials) will never be stored in code or config in plaintext – they’ll be managed via AWS Secrets Manager or environment variables in a secure manner.
● Authentication & RBAC: We will implement role-based access control (RBAC) such that each user has a defined role (or multiple roles) with specific permissions. For example, a “Project Manager” role might query Riddler for project data but not have access to financial queries, whereas a “Finance” role can use the Fang agent and see financial data. We will likely integrate with Acme’s existing Single Sign-On (SSO) if available, or provide a secure username/password or OAuth-based login system. Upon login, the React frontend gets a JWT token that encodes the user’s identity and roles. The backend on each request checks this token and authorizes the action. Sensitive API routes will have role checks – e.g., only managers can execute certain Riddler queries. This ensures least privilege: each agent only provides data to those who should see it. The system will maintain user permission levels exactly as Acme defines. (For instance, Riddler queries will automatically filter data based on the user’s department or clearance: if a general employee asks a question that involves executive-only data, Riddler will either refuse or omit that info.)
● Local Data Handling & Privacy: We understand Acme’s requirement that their data remain local and not be used for public AI training. To address this, our architecture keeps all sensitive data within Acme’s AWS environment, and we configure AI API usage in a privacy-safe way. Specifically, when using OpenAI/Anthropic, we leverage enterprise settings or API parameters to ensure no data is retained by the provider for training purposes. (OpenAI’s policy by default for API usage is not to use data for training unless opted in, and we will not opt in.) If desired, we can also route API calls through an Azure OpenAI instance or a dedicated environment for added assurances. Furthermore, if any extremely sensitive data should not even leave the environment, we have the flexibility to use on-premise models or open-source models within the VPC for those specific cases (for example, a small LM for classifying highly sensitive documents, though this might not be necessary given the aforementioned privacy guarantees). The system does no fine-tuning of the AI models on Acme’s data (fulfilling the “no model training” mandate) – the AI models are used in a zero-shot or few-shot mode only. All Acme’s data (emails, documents, etc.) remains stored in Acme’s own database and S3; the AI sees it only transiently when formulating a response, and then it’s gone from the AI’s memory.
● Audit Trails: Every significant action and decision by the agents will be logged for auditability. We will implement detailed logging such as: when an email is classified by SIFT, the system logs the email ID, timestamp, predicted category, and the user (or system) that ultimately handled it; when Riddler answers a query, it logs the question asked, the user who asked it, and which data sources were consulted. These logs will be stored in a secure, append-only manner (e.g., in MongoDB or a separate logging database/table) to serve as an audit trail. We’ll also capture model outputs and prompts for debugging and compliance (especially for Riddler, to trace how answers were derived, which is important for trust). Access to logs will itself be restricted to admins or compliance officers. Audit logs provide accountability: if an agent makes an erroneous decision, we can trace why; if a user queries sensitive info, we have a record of it. Additionally, we can configure alerts for certain events (e.g., if Fang flags a likely fraud, or if Riddler is asked a question that returns no answer due to permission, etc., that could trigger an email to an admin for review).
● AWS Cloud Security: The AWS infrastructure will be configured with security best practices. All components reside in a Virtual Private Cloud (VPC) inaccessible from the public internet except via the secure Application Load Balancer that fronts the FastAPI service. The EC2/ECS instances for the backend will be in private subnets (no direct public IPs). Security groups (firewall rules) will restrict inbound traffic to only the load balancer and necessary ports, and outbound traffic only to specific endpoints (e.g., allow outbound to OpenAI API domain). We will employ AWS Identity and Access Management (IAM) roles to ensure, for example, the backend instance can read from the S3 bucket but nothing else can, etc. If using ECS or Lambda, those will also run under IAM roles with least privileges. We will also enable AWS CloudTrail and CloudWatch for monitoring access to resources. VPC flow logs can be enabled to detect any unusual traffic. In short, the cloud environment will be locked down to just what’s needed for the solution to function.
● High Availability & Backups: For business continuity, we will deploy the system in a highly available manner. This could include running multiple instances of the FastAPI service across availability zones behind the load balancer (so if one server goes down, another handles requests). The MongoDB database (if Atlas) will be a multi-AZ cluster or if self-hosted, we’ll run a replica set. Regular backups of the database will be scheduled (daily snapshots, etc.), and S3 versioning ensures file backups. In the unlikely event of a severe outage (e.g., AWS region failure or the AI API downtime), the system is designed to fail gracefully: if the AI API is unreachable, agents can queue requests or retry later, and users will be notified of temporary unavailability. And since core data (emails, bids) still reside in standard systems (email server, etc.), Acme can always fall back to manual processes until service is restored – meaning no single point of failure will halt operations completely. Part of our security commitment is also disaster recovery: we’ll have a runbook for how to restore the system from backups in case of catastrophic failure, aiming for an Recovery Time Objective (RTO) in hours and Recovery Point Objective (RPO) near zero (with continuous data replication).
● Compliance: We will adhere to any relevant compliance requirements. If Acme deals with any regulated data (for example, if any personal identifiable information (PII) is involved in HR emails or if there are government contract docs), we ensure compliance with data protection regulations (such as GDPR/CCPA principles for personal data – providing ability to delete or anonymize if needed). The design of keeping data local and not training models on it inherently helps with privacy compliance. We will also likely ask Acme to classify data sensitivity levels so that we can apply extra controls if necessary (for example, mark some documents as “internal confidential” which Riddler might only answer in summary form to normal users, etc. – effectively implementing a data governance policy in the AI’s behavior). All these measures, combined with Secure Data Handling commitments (ensuring data stays local and private), address the critical security and privacy concerns expressed by Acme’s leadership.
3.3 AWS Deployment Diagram
To give a clearer picture of the infrastructure, below is a deployment diagram focusing on AWS components and how the system will be set up
AWS deployment components. This shows Acme’s AWS account with a dedicated VPC. User traffic enters via an Application Load Balancer (with HTTPS and WAF rules if needed for protection), which routes to an ECS Cluster running the FastAPI backend in containers (or an EC2 Auto Scaling group running the app). The backend containers retrieve secrets (API keys, DB URIs) from AWS Secrets Manager at startup, ensuring sensitive config is not hard-coded. The backend connects to the MongoDB database (either an Atlas cluster or AWS DocumentDB, likely deployed in the same VPC or accessed through a secure link). Optionally, a Redis cache is shown if we add caching. All application logs and metrics go to CloudWatch, enabling monitoring of performance and quick troubleshooting. File storage is handled via a private S3 bucket; the app has an IAM role that grants it necessary access to read/write this bucket. This infrastructure is defined and managed via Terraform (see CI/CD section), ensuring consistency between environments. It is also designed to be scalable: ECS can increase tasks if load grows, and highly available: if one availability zone goes down, others pick up traffic.
With this architecture, we emphasize security, isolation, and scalability. Acme gets a robust system where AI agents are deeply integrated into their operations environment without compromising on safety or performance.
4. CI/CD & DevOps Strategy
To deliver this project efficiently and reliably, we will employ robust CI/CD and DevOps practices. The goal is to make development and deployment predictable, fast, and rollback-safe, with the ability to stage changes for each agent and infrastructure managed as code.
● Source Control & Branching: All code (frontend, backend, agent prompts, infrastructure definitions) will be stored in a git repository. We will use a branching strategy that supports parallel development of multiple agents. For example, each major feature or agent could have its own feature branch. Developers will create merge (pull) requests that are reviewed before merging to the main branch. The main branch corresponds to the production-ready code, whereas a develop branch might correspond to the staging environment. We ensure that every change is tracked, and we tag releases (e.g., v1.0, v1.1) especially for major milestone deployment
● Continuous Integration (CI): On each push or PR, an automated CI pipeline (using a service like GitHub Actions, GitLab CI, or Jenkins) will run. This includes:
○ Automated Testing: We will write unit tests for critical functions (e.g., email classification logic, data parsing functions) and integration tests for API endpoints. Additionally, we can include tests for prompt outputs (using saved example inputs to ensure the LLM responds in expected ways, within some tolerance). The CI pipeline will run these tests to catch issues early.
○ Linting & Static Analysis: Tools like flake8/Black for Python and ESLint for JavaScript will ensure code quality and consistency. Security linters (Bandit for Python) will also run to catch any obvious security flaws (e.g., use of eval, insecure config).
○ Only if the pipeline passes (tests green, no lint errors) will the code be allowed to merge/deploy, which instills confidence in stability.
● Continuous Deployment & Staging: We will maintain at least two environments: Staging and Production. Staging is a sandbox that mirrors production (same infrastructure configuration, but using test data or a subset of real data). After CI, an automated deployment to Staging will occur. For example, we can have an AWS ECS service for staging that pulls the latest image tagged as staging. This allows the team (and Acme’s key users, if desired) to test new features or agents in a safe environment. Each agent can be tested in staging with real or realistic data. We can even have per-agent sandbox toggles: e.g., we might deploy a new version of the Fang agent to staging and let the finance team test it on recent receipts, while other agents remain unchanged. This granular approach ensures we validate each agent’s behavior before it hits production.
● Versioning and Rollback: Each deployment will be versioned (e.g., “Email Agent v1.2 deployed on Aug 10”). We will use Docker images with tags (like core-agents:v1.2) so that any previous version can be redeployed quickly if an issue is found. The infrastructure (Terraform code) will also be version-controlled, so we know which version of infra is tied to which release. Rollback procedure: if a new agent update causes a problem, we can promptly rollback by re-deploying the last stable container image (this can be done in minutes via ECS or our deployment scripts). Also, because agents are somewhat independent, a faulty agent can be disabled or isolated without taking down the whole system. For example, if an update to Launch Packages has a bug, we can temporarily disable its trigger and hide its UI entry, while fixing it in staging, and the rest of the system continues running unaffected. This isolation adds confidence that we can safely iterate without major disruptions.
● Infrastructure as Code (Terraform): We will define the entire cloud infrastructure using Terraform scripts. This includes VPC setup, subnets, security groups, ECS cluster and services, load balancers, S3 buckets, etc. Infrastructure as code ensures that we can recreate the environment reliably (e.g., to set up a dev/staging environment identical to prod). It also provides audit trail of changes to infra. For instance, if we need to open a new port or increase instance size, it’s done via code review on Terraform files. Terraform will be integrated into the CI/CD pipeline – possibly using a Terraform Cloud or Atlantis, or simply manual terraform apply steps for major changes. We will plan changes and get approvals before applying to production.
Example: Below is a snippet of a Terraform configuration for the S3 bucket used by File Sweeper, illustrating our approach to secure infra by default:
● Terraform snippet: This defines an S3 bucket with versioning enabled and server-side encryption (AES-256) by default. Tags are applied for clarity. Similar Terraform resources will define the ECS tasks, load balancer, etc., all with best practices (e.g., enforcing TLS, creating IAM roles with least privileges for the ECS tasks, security group rules only for required ports). Using Terraform means any team member (with proper permissions) can review the exact configurations, and changes are tracked in git.
● Deployment Process: We anticipate using containerization for the backend (Docker). The CI pipeline will build a Docker image for the FastAPI app (including all agent code and prompt templates), push it to a registry (like Amazon ECR), and then trigger an update to the ECS service. For the frontend, if it’s a single-page app, we might build and deploy it to an S3 bucket or CloudFront. That can also be automated in CI (upload new static files on release). We will implement blue-green or rolling deployments for minimal downtime. For example, ECS can spin up new containers with the updated version while old ones are still running, then cut over the traffic when healthy – ensuring “fast and safe” deployment.
● Sandbox Testing per Agent: To instill confidence in each agent’s behavior, we will create sandbox test cases and data. For example, for the Bid Centry agent, we maintain a set of sample bid documents; each new update of Bid Centry is automatically run against these samples in CI to verify it still correctly extracts key fields. Similarly, for SIFT, we have a test mailbox of various types of emails to ensure the classification is correct after any prompt changes. This functions as regression testing for the AI behavior, which is important since LLM-based components can be non-deterministic. We can log expected outcomes in these tests (with some tolerance for variability). This approach treats prompt and chain configurations as code that can be tested, giving a safety net when refining agent prompts.
● DevOps Tooling: We will leverage modern DevOps tools to monitor and maintain the system:
○ Logging/Monitoring: As mentioned, CloudWatch (and/or ELK stack) will aggregate logs. We will set up dashboards for key metrics: e.g., number of emails processed per hour, average response time of Riddler queries, errors if any from the AI API, etc. We will also configure alerts (SNS or similar) for critical conditions (like if an agent fails repeatedly or latency spikes beyond SLA, or if usage suddenly drops/increases unexpectedly).
○ Continuous Feedback Integration: DevOps isn’t just about code – it’s also about incorporating user feedback quickly. We may set up a feedback form or issue tracker where Acme’s users can report problems or suggestions. Our team can triage those and, thanks to CI/CD, push out improvements perhaps on a weekly sprint cycle or even faster for minor fixes.
This CI/CD and DevOps setup ensures that throughout the project (and beyond), we can deliver updates confidently and respond to Acme’s needs rapidly. Every phase (as in the timeline) will result in a deployable increment, and our pipeline will safeguard quality. The use of Terraform and robust version control also demonstrates to Acme that the system can be maintained or handed off cleanly – everything is documented in code.
5. Agent Design & Prompt Examples
Each agent in the system is designed with a clear input-output contract, a robust backend logic (often combining AI prompts with rules), and target performance metrics (accuracy and latency SLAs). Below, we describe the design of each agent in technical terms, and provide example prompts or pseudo-prompts that illustrate how we will harness language models for their functionality. These prompt templates will serve as a starting point (“bootstrap”) for development, to be refined with Acme’s data.
● SIFT (Email Triage Agent):
Input/Output: SIFT takes in raw email data (subject, body, sender, etc.) and outputs a classification of the email’s category and priority. For example, an input is an email text, output might be: Category: Bid Inquiry; Priority: High.
Backend Logic: SIFT uses a GPT-O3 (or GPT4.1) model prompt to categorize emails. We define a fixed set of categories relevant to Acme’s business (e.g., Bid/Proposal, Project Update, Financial/Invoice, HR, Other) and priority levels (High/Urgent or Low/Normal). SIFT might first use an LLM to determine the category, and possibly a secondary check or prompt for priority (or combine both in one prompt). There may be a small rule layer on top: for instance, if an email comes from a known client and mentions “urgent” or “ASAP”, we ensure priority is High. The agent will then tag the email (in the database or via the email server) with these results.
Model & Performance: Using GPT-O3 ensures it understands nuanced email content to classify accurately. We aim for >95% accuracy in categorizing critical emails correctly (especially identifying those that are urgent or bid-related) and <2 seconds processing time per email (excluding any email server latency). To achieve speed, we may use GPT4.1 for straightforward classification tasks and reserve GPT-O3 for ambiguous cases. The agent is stateless per email (each email is handled independently).
Prompt Template Example: Below is an example prompt we’ll use to guide the model’s classification:
● Expected model completion: “Category: Bid/Proposal; Priority: High.”
Explanation: This prompt explicitly defines the categories and priority schema. In development, we will refine it by adding examples (few-shot) from Acme’s actual emails so the model learns the style. For instance, we might include a sample email and the correct output as part of the prompt. The prompt instructs the model not to do anything but categorize and label priority. We will test and adjust wording to ensure the model’s output is consistently structured (likely we’ll ask it to output in a machine-readable format, e.g., JSON like {"category": "Bid/Proposal", "priority": "High"} for easy parsing).
Triage (Communication Routing Agent):
Input/Output: The Triage agent takes the classified email (category & priority from SIFT, plus potentially the email content summary) as input and outputs a routing decision: essentially, who or which team should handle this communication, and how (forward email, create task, notify, etc.). For instance, input: Category: Bid/Proposal, Priority: High, Content Summary: "Bid due tomorrow for X project"; output: Route to Estimating Team (notify lead estimator).
Backend Logic: Triage uses a combination of rule-based logic and AI. Acme’s org structure will be encoded: e.g., Bid/Proposal -> Estimating Department, Finance -> Accounting Department, HR -> HR rep, etc. This mapping covers many cases straightforwardly. However, within a category, or if an email is complex, we might use an LLM to analyze who specifically should get it. For example, if category is “Project Operations,” the agent might parse which project it’s about and then route to that project’s manager. Triage might call a small prompt like: “Given this email about Project X, who in the organization is the right person or team to respond?” or it might simply use a lookup table if Project X’s manager is known. We will maintain a config (in Mongo or code) for routing rules (e.g., keywords or categories mapping to team emails or user IDs). The Triage agent will then perform the action: either automatically forward the email to a specific address, or generate a task/ticket in whatever system Acme prefers (we can integrate with a task management tool or simply send a notification email that says “Please address this”).
Model & Performance: A lot of Triage can be done with deterministic rules, which is fast (~sub-second). For edge cases, using GPT-O3 can help interpret unstructured instructions (“Bob, can you handle this permit issue?” might be categorized as Operations but actually meant for Bob in permits team). We expect near 100% correct routing when combined with human-validated rules (because any uncertain cases can default to a fall-back – e.g., send to a general inbox or flag for manual review). Latency is minimal, maybe 1 second added after SIFT.
Prompt Example: (If using AI for a complex route)
● Expected output: “Estimating Team.” (The agent would then know to forward the email to the Estimating Team’s group address or notify that team in the app.)
In practice, we will integrate this with actual routing – likely no user sees “Estimating Team” as text; instead the system will automatically forward or assign. The prompt helps to get that decision internally. As we deploy, we might find we can bypass the LLM for Triage if rules suffice. The design allows either approach.
● Bid Centry (Bid Document Management Agent):
Input/Output: Input is a bid document or RFP (usually a PDF or Word document, possibly multi-page). Output is a structured summary of key information, such as: Project Name, Client, Bid Due Date, Scope Summary, Key Requirements, Estimated Budget (if any), etc., stored in a database record and optionally a short summary for an email/notification. Essentially, Bid Centry “reads” the bid document and translates it into a checklist or data entry that Acme’s team would normally do by hand.
Backend Logic: The Bid Centry agent will likely have multiple steps. First, the document is ingested – if it’s a PDF, we use an extraction pipeline: possibly AWS Textract or PyMuPDF to get raw text. For structured parts (like a bid form), Textract might identify fields. Once text is extracted, we then feed it to an LLM (GPT-O3, given the complexity and need for accuracy) with a carefully crafted prompt to find the required details. We will train the prompt on what to look for: e.g., “Find project name, client name, bid due date, location, and any other important details.” The model can then output a JSON or YAML of the fields. We will also include few-shot examples if possible (like providing a dummy RFP excerpt and the expected JSON). After the LLM returns the data, the agent will parse it and possibly cross-verify obvious things (like ensure a due date is in date format and in the future). The data is then saved in a Bids collection in MongoDB. We can also trigger follow-up actions: e.g., if a bid due date was extracted, the system could create a calendar event or reminder (though that might be under Launch Packages domain or simply an alert).
Model & Performance: GPT-O3 is preferred due to its superior comprehension of long documents and instructions. We anticipate one document’s processing could take a few seconds up to perhaps 20-30 seconds if very long (tens of pages). This is acceptable since this is not a high-frequency operation; it’s more important to be thorough. We aim for near 100% extraction of critical fields like due date (we cannot afford to miss a deadline) – hence we might incorporate redundancy: e.g., parse for date patterns in text via regex as backup to ensure we capture a date even if the LLM fails. We’ll target an accuracy SLA such as “All critical fields (project, client, due date) correctly extracted in 99% of cases, and no false data”. If the model is unsure (e.g., document is very unstructured), we might have the agent flag it for human review instead of guessing.
Prompt Template Example:
● For example, from a document, the model might output:
● We will refine the prompt to match Acme’s typical documents. Notably, we’ll ensure the model knows to pull dates in standardized format, etc. If a document is extremely large, we may chunk it (e.g., split by sections) and have the LLM analyze sections or use LangChain’s document QA approach to ask specific questions (“What is the due date?” etc.) to ensure nothing is missed. Those details will be decided during implementation, but the prompt above is a strong starting point for a one-shot extraction.
● File Sweeper (File Organization Agent):
Input/Output: Input is any new or existing document (could be a PDF, Word doc, text transcript, image, etc.) that needs organizing. Output is the placement or tagging of that file in the system, along with any extracted metadata. For example, if a meeting transcript is uploaded, File Sweeper might output: Type: Meeting Transcript; Project: Alpha Site; Date: 2025-08-01; Tags: safety, meeting, and it ensures the file is stored in the “Transcripts/Alpha Site/2025” folder and the metadata is saved in the DB.
● Backend Logic: File Sweeper’s operation is to automate what a diligent staff member might do when saving files: read it enough to know what it is and where it should go. The agent will likely use LLMs for content understanding: e.g., take the text or OCR of a file, generate a brief description or identify key words (like project names, document type). We will maintain a taxonomy of file types and storage rules – for instance: If a document contains keywords “Meeting Minutes” or has a dialogue format -> classify as Transcript. If it contains “Invoice” -> Finance Docs. If it has a permit number -> Permits folder. File Sweeper can use a mix of keyword rules and an LLM classification (similar to SIFT but for documents). It will then move/upload the file to the corresponding location in the file system (which, under the hood, is the structured S3 bucket or perhaps an on-prem server if needed, but likely S3 given our architecture). It will also create a record in the DB with metadata including: original filename, determined category, tags, related project (if identifiable), and any dates (e.g., document date). Chrono Sweeper (next agent) complements this by focusing on date organization. The two might work together: File Sweeper assigns categories and tags, Chrono Sweeper might adjust where it goes based on date (like ensuring year-based folders or archiving old ones).
Model & Performance: For document classification and summarization, GPT4.1 should suffice as these are not extremely nuanced beyond the content domain it’s likely familiar with. If needed (for very domain-specific stuff), GPT-O3 can be used, but we aim to use the faster model here to process potentially many files quickly. Accuracy goal: correctly classify/route files >90% of the time (with a human able to correct misfiled ones if needed via the UI). The system will log uncertain cases (e.g., if confidence is low, perhaps flag a human to confirm file category). Latency is not critical here since file organization can be async – a user might upload a batch and within a minute see them sorted. We’ll ensure throughput to handle many files (by parallelizing or scaling if needed).
Prompt Example:
● Expected model interpretation might be: Type: Meeting Transcript; Tags: ["Alpha Site", "Meeting Minutes", "Safety"]. In reality, we’d incorporate more direct prompting: maybe instruct it to output in a structured way, or even to decide a folder path (like Projects/Alpha Site/Transcripts). However, folder logic we might handle in code by using the project name and type from the model. The prompt will be refined with more context (like a list of possible file types it should consider). This agent’s prompt and logic will evolve as we gather examples of Acme’s documents.
● Chrono Sweeper (Chronological Management Agent):
Input/Output: Chrono Sweeper doesn’t necessarily have a single “prompt input” from a user; it operates on the repository of files to ensure chronological order and timely management. Its outputs are actions like: archiving files past a certain date, or adding timestamps to metadata, or generating reminders for upcoming dates found in documents. For example, Chrono Sweeper might scan the “Permits” folder and find that a permit will expire on 2025-10-01 and output an alert “Permit XYZ expires in 7 days” to the relevant team. Or it might move files from an “Active Projects” area to “Archived Projects/2025” once the project is completed and a certain time has passed.
Backend Logic: Chrono Sweeper is like a scheduled maintenance agent. It will periodically run (say nightly or weekly) and use rules plus possibly AI to manage time-related aspects. Key functions:
○ Date Extraction: For each new file or record, if not already done by File Sweeper, Chrono Sweeper can extract dates (like meeting dates, due dates, expiration dates) from content. LLMs can be used to understand context (“this is an expiration date for a permit”). But often simple parsing or using the metadata (file timestamps, date in file name) suffices.
○ Archiving: Based on policies defined by Acme (e.g., archive project documents 6 months after project end), Chrono Sweeper will move or flag files. This can be a straightforward script using the file’s date metadata or project end date from a database. We’ll likely store relevant dates in the DB when known (Launch Packages might mark project start/end).
○ Notifications: Chrono can send notifications for time-sensitive events. This might involve using an LLM to draft a message like “Reminder: The bid for Project X is due in 2 days (on June 5).” Or simpler, use a template where we plug in data.
○ Chrono Sweeper ensures chronological integrity – e.g., naming conventions or folder structures by year. If a file arrives with a date, Chrono might ensure the file path contains that year or create a subfolder for that year if not present.
Model & Performance: Chrono Sweeper’s heavy lifting is likely done by logic and simple NLP rather than requiring GPT-O3. If we use AI, it might be GPT4.1 or even a smaller model for specific tasks (like understanding a sentence to pick out a date and what it means). The performance is about reliability – we want no document that should be archived to be missed, no important date to slip. We will test it thoroughly with sample data. Because it runs in background, latency is not user-facing, but we’ll ensure the tasks complete within a window (for nightly jobs, within a few hours if lots of data).
Prompt Example: Chrono might not use a single prompt often, but for illustration, if using an LLM to interpret a document date significance:
● Actually, rather than a direct prompt, we might encode logic: if any “expires on ” found and date is in future, create an alert 30 days before that date. This agent is more algorithmic. The design will involve writing scripts to query the database for any upcoming dates (from bids, permits, etc. which other agents have stored) and then using either an email template or LLM to generate a friendly reminder to send via email or in the dashboard. So Chrono Sweeper is a bit of an orchestrator that ties together date info across the system.
● Launch Packages (Project Launch Agent):
Input/Output: Input is an event or command indicating a new project start (this could be triggered when Acme wins a bid, or manually by a user clicking “Launch Project” in the UI, providing project details). The output is a set of actions and documents: for example, a generated checklist of tasks, pre-filled forms (like initial permit applications or safety checklists), project folder structure created, and an introductory summary for the team. Essentially, Launch Packages produces the initial “kit” for a project.
Backend Logic: Launch Packages will draw on information from the Bid Centry agent (since once a bid is won, we have data like project name, client, scope, etc.). It will use templates for standard project startup documents. For instance, Acme might have a standard “Project Kickoff Checklist” (with items like “Safety briefing scheduled, Utilities disconnect scheduled, Site map prepared”…). The agent will fill in project-specific data into these templates. We can store these templates in a database or even as Word docs that the agent populates. We will use the LLM to generate any text that needs tailoring. For example, it might compose a “Project Launch Summary” – a paragraph or two summarizing the project and key dates, that could be sent to all stakeholders. It might also prepare an email draft to the project team introducing the project. Launch Packages will also coordinate with the file system: creating the project folder (or instruct File Sweeper to do so) with subfolders for permits, plans, etc. If integrated with any project management software, it could also create a new project entry there. Initially, we can confine it to our system: e.g., present the launch checklist in the UI for the user to download or mark tasks done.
Model & Performance: GPT-O3 will be useful here for generating high-quality, tailored text (like summarizing the project or writing a welcome message), as well as ensuring the checklist covers everything by interpreting the bid info. For example, if the bid mentions hazardous materials, perhaps the launch package should include a hazardous material abatement plan – an AI could catch that. However, we will likely have a static template to avoid missing critical tasks. The AI’s main job is customizing and sanity-checking. Performance is not time-critical; even if it takes 20 seconds to generate all documents, that’s fine as it’s an occasional action. Accuracy is measured by completeness – we want 0 items missed that the team would normally do. We’ll work closely with Acme’s ops team to enumerate those tasks so the agent doesn’t rely solely on AI creativity.
Prompt Example: Suppose we want to generate a project kickoff summary:
● Expected output (from GPT-O3): “Our team is gearing up to begin the Springfield Mall project for the Springfield Redevelopment Agency, with a scheduled start on November 1, 2025. This project will involve the teardown of the two-story mall and proper disposal of all debris. Notably, asbestos abatement will be a critical early step to ensure safety. Work will be limited to nighttime hours per city requirements. The project is expected to run for approximately 6 months. Please ensure all preliminary checks (permits, safety plans, notifications) are completed prior to kickoff.”
Launch Packages agent would generate similar content, as well as ensure checklists are populated. It might use multiple prompts internally – one for summary, one for checking if any special permits are needed (it could ask itself something like: “Does the scope mention any hazardous materials or special conditions? If yes, make sure to include corresponding tasks.”). This agent is like a project manager’s assistant, so we will iterate with Acme’s actual process to fine-tune what it produces.
● Fang (Fraud Detection Agent):
Input/Output: Input is financial documents such as receipts, invoices, or expense reports, either individually or in batches. The output is an analysis marking any items of concern – for example: “Receipt #123: Flagged – vendor not in approved list” or “Invoice from XYZ Co.: Flagged – amount $50,000 exceeds expected $30,000 for Project Q.” The output could be a report or notifications for finance managers. Ideally, Fang integrates with the finance workflow so that whenever a receipt is logged, it gives a thumbs-up or a warning.
Backend Logic: Fang will use a combination of rule-based anomaly detection and LLM-based pattern recognition:
○ OCR/Data extraction: If receipts are images or PDFs, Fang first uses OCR (AWS Textract or similar) to get structured data (date, vendor, amount, line items).
○ Rules/Heuristics: We incorporate known fraud indicators: e.g., if an expense date is on a weekend or outside project duration, if the same receipt appears twice, if a vendor name is slightly off (potentially a fake company name), or if amount is above a threshold. These rules can catch straightforward issues.
○ AI Analysis: We then use an LLM (GPT-O3) to analyze the context of the expenses for subtler anomalies. For example, feed it a summary of expenses and ask “Do any of these look suspicious or inconsistent with the project scope?” The model might catch something like an invoice for equipment rental when the project scope said equipment is owned, or a pattern of rounding all amounts to just under approval limits. We can also ask it to explain why something might be an issue, to include in the report.
○ Cross-check Data: Fang can cross-reference with known data from the database: e.g., compare an invoice amount to the budget recorded in Bid Centry’s data for that project, or see if a vendor is known (we can maintain a whitelist of approved vendors). It may use direct queries (without AI) to fetch such data and then either decide via code or feed both the invoice and relevant reference info into the model for a judgment.
○ After analysis, Fang compiles a result: probably an entry in a “Fraud Alerts” collection and possibly an immediate email alert for high-risk findings. It will also log all clear checks for traceability (“Invoice 456: OK”). Over time, as Fang learns (with feedback from finance team marking false positives/negatives), we can refine its rules and prompts.
Model & Performance: Fraud detection benefits from GPT-O3’s reasoning, but we will constrain it with concrete data to avoid hallucinations. For performance, processing an invoice might take a few seconds (OCR might be the slowest step). This is typically fine as finance processes aren’t real-time critical. Accuracy is crucial – we aim to catch the majority of true issues while minimizing false alarms. We might set an SLA like “Detect 95% of intentionally fraudulent modifications in a controlled test, with less than 5% false positive rate” – and continuously improve toward that. We’ll involve Acme’s finance experts to get realistic fraud scenarios to test Fang.
Prompt Example: After extracting data from a receipt:
● Expected GPT-O3 Analysis: “Flagged as unusual. Reason: The vendor ACME Supplies is not on the approved list (could be an unregistered variant of Acme Corp). The amount $4,500 is over twice the typical cost for safety gear in similar projects, which is suspicious. Additionally, the payment in cash is atypical for such a large purchase. These factors suggest this expense should be reviewed for potential fraud.”
The Fang agent would take such an output and generate a structured alert for the finance team. The prompt example shows how we give the model concrete facts (extracted data and cross-check info) so it can make an informed decision. We’ll fine-tune the triggers for Fang by adjusting these prompts and rules as we see how it performs on Acme’s actual expense data (or seeded test data).
Agent Accuracy and Latency SLAs: Each agent will have defined SLAs that we will monitor. Summarizing a few:
● SIFT: ≥95% accurate classification of urgent vs non-urgent; processes each email in <3 seconds.
● Triage: ~100% correct team routing (after initial tuning) for the defined categories; processes in <1 second for rule-based routing (maybe up to 3 seconds if involving an LLM query for complex cases).
● Bid Centry: Extracts 100% of critical fields (project name, due date, client) and ≥90% of secondary info accurately; turns around a document in <30 seconds.
● File/Chrono Sweeper: Correctly classifies files ≥90% and archives per policy with 0 omissions; since it’s background, latency measured in bulk (e.g., archives completed by the next day).
● Launch Packages: Fills 100% of required checklist items; generates outputs within 1 minute of trigger.
● Fang: Catches ≥95% of known fraudulent scenarios, with false positive rate under 5%; processes a batch of receipts (say 10) in <1 minute.
● Riddler: (Not described yet here) – but for completeness, Riddler’s SLA might be answer accuracy (determined via user feedback) and latency of, say, <5 seconds for typical queries (may vary if it has to read many docs, but we'll aim to optimize with retrieval techniques).
Finally, note that all prompt templates provided above are starting points. We will iteratively refine them using Acme’s actual data and feedback (the prompts may get longer with few-shot examples, or more instructions to handle edge cases). The flexibility of LangChain allows us to version and swap prompts as needed without code changes to logic. By providing these initial templates, we have a head start in development for each agent.
6. User Interface Design
The user interface will be a crucial component to ensure Acme’s team can effectively interact with the AI agents and trust their outputs. We will design a clean, intuitive web application (React-based) that serves as the “AI Agent Console” for Acme’s. This UI will allow users to test agents, view results, and provide feedback. Below we describe the planned UI/UX for key parts of the system, effectively “mocking up” how users will experience the agents:
● General Layout: Upon login (which will be secured via password or SSO), users will see a dashboard homepage providing an overview of the AI agents’ activities and any important alerts. For example, the dashboard might show metrics like “5 Emails triaged today, 1 High Priority”, “2 Bids processed this week”, “1 Fraud Alert – click to view”. It will also show a navigation menu or icons for each agent/tool available to that user (respecting RBAC, so some users might not see Fang if they’re not in finance).
● Email Triage UI (SIFT & Triage): Users (especially those handling incoming communications) will likely still use their normal email client for day-to-day work, but the system can augment that via integration. However, within our portal, we will have an Email Triage view where they can:
○ See a list of recent emails with SIFT’s classification labels and priorities. We might present this similar to an email inbox, but with a priority sort. High priority emails could be highlighted or grouped at top with a red bar.
○ The user can click an email to see details: the content, the category SIFT assigned, and the suggested routing (Triage result). They could be given an option to provide feedback (“This was misclassified” or “Wrong team suggested”) which would feed back to our system for improvement.
○ During initial testing, we might have a toggle that lets the user simulate the triage: for instance, drag-and-drop an EML file or paste email text to see how SIFT categorizes it. This is part of “testing agents by end-users” – a safe sandbox for them to gain confidence.
○ After deployment, the UI will more likely be used for oversight rather than daily checking. For example, a supervisor might open the triage console to see if any urgent emails were flagged and ensure none were missed or incorrectly handled.
○ If integrated directly with Outlook/Gmail, SIFT could apply labels like “[High Priority][Bid]” to the email subject or move it to a folder. We will aim to implement such integration (possibly via Microsoft Graph API for O365) so that the user experience is seamless. The UI will then act as a monitoring and configuration tool for triage.
● Bid Management UI (Bid Centry): We will provide a “Bid Documents” page where users (estimators) can upload and review bids:
○ An Upload button will allow a user to upload a new RFP or bid document. As soon as they upload, a progress indicator will show that the document is being analyzed by the Bid Centry agent. Once done, the extracted key info will be displayed on screen.
○ The UI will show fields like Project Name, Client, Due Date, etc. in an editable form – so the user can double-check and correct any field if needed (for instance, if the AI missed something or parsed a date incorrectly, the user can override it before saving).
○ After confirmation, the data is saved, and perhaps a “Bid Summary” view is presented – showing all active bids in a table with their due dates, status, etc. This essentially becomes a mini bid tracker dashboard (something Acme likely can use to manage their pipeline).
○ Historical bids or processed documents can be searched and viewed. A search bar might allow filtering bids by client or keyword (with Riddler’s help behind the scenes if we implement search via Q&A).
○ This interface will significantly speed up how Acme’s team goes from receiving an RFP to recording it in their systems. It’s user-friendly: no more manual retyping, just review AI’s suggestions and approve.
● Riddler UI (Central Q&A Console): Riddler will have a chat-like or search interface as it’s essentially an AI assistant for querying company data. We envision a chatbot interface on the portal:
○ A text box where users can type a question in natural language (e.g., “What is the next bid deadline coming up?” or “Show me all expenses on Project Alpha above $10k”).
○ The UI displays the AI’s answer just below the question, much like ChatGPT, possibly with a little “thinking” indicator while it processes.
○ Importantly, because this is for business intelligence, we may also display source links or references with the answer (e.g., if Riddler’s answer uses data from a specific document or database entry, it might show “Source: Bid Tracker - Project Alpha” or “Source: Invoice #123” as a hyperlink). This builds trust in the answers by letting users verify.
○ Users can upvote or downvote answers or flag if something seems off, as a feedback mechanism.
○ There might be pre-saved queries or buttons for common questions, to guide users (e.g., a sidebar that says “📊 Key Queries: [Project cost summary], [Upcoming deadlines], [Recent fraud flags]” which when clicked, populate the question box).
○ Access control: If a user lacks permission for some data, Riddler’s answer will be sanitized. The UI might show a message like “(Certain details are hidden due to access level)” if applicable. This ensures a clear user experience even when data is restricted.
○ During the initial rollout, the Riddler interface can be used in a testing mode by key users to see how well it answers. Over time, as confidence grows, this could become a daily tool (like how one would use a search bar on an intranet).
● File Browser UI (File/Chrono Sweeper): We will integrate a file management interface for the organized documents:
○ Think of it as a custom Google Drive or SharePoint view, but powered by AI metadata. Users can browse by project or category. For instance, a left sidebar might list Projects, and under each project, subfolders like “Transcripts”, “Contracts”, “Permits”, etc., which the File Sweeper organizes.
○ There will be a search function (possibly powered by Riddler’s backend) where a user can search by keyword across all documents and transcripts. The AI can not only find matches but maybe answer questions like “Find the transcript where we discussed asbestos abatement” – returning the file or even the snippet.
○ Chrono Sweeper’s influence: we might have a timeline view – e.g., a calendar or timeline that shows documents by date. Perhaps an interactive timeline for each project or for bids: scroll through months and see what documents (or major events) are at each point. This could be very helpful to visualize, say, the sequence of communications and files for a project.
○ The UI will also highlight upcoming due dates or expirations (Chrono’s alerts). For example, a banner on the file page: “Reminder: Permit ABC expires in 5 days (see Permits folder).”
○ The design will prioritize easy retrieval: even though AI can answer questions, sometimes the user just wants to navigate to the right file. So we ensure the portal lists things in a familiar hierarchy while using AI to enhance tagging and search.
● Launch Packages UI: There will be a section for Project Launch. When a project award is confirmed, a user (likely a project manager or estimator) can go to “Launch Project” page:
○ A form or wizard where they input basic info: select the bid (maybe from a dropdown of recently won bids that Bid Centry entered), confirm start date, any special notes.
○ They hit “Generate Launch Package”. The UI might show a loading/progress as the agent compiles everything.
○ The result is presented as a checklist with downloadable items. For example:
■ “✅ Project folder created” (with a link to the folder).
■ “✅ Kickoff Checklist generated” (link to a PDF or online checklist).
■ “✅ Initial Project Summary email drafted” (with a button to view/edit the email before sending).
■ “⚠️ Special permits required: Yes – environmental permit needed. (Review details)”
■ bThe interface likely will allow the user to review and then approve sending out any notifications. We won’t auto-email without user confirmation for such critical communications, at least not initially.
○ Essentially, this page is like an interactive report of what Launch Packages agent prepared, giving the user confidence to use it. They can iterate (if they change some input or want to regenerate, they can).
○ Over time, if this becomes highly trusted, some steps could be automated fully (e.g., auto-create stuff in background), but we’ll always allow user oversight via the UI.
● Fang (Fraud Alerts UI): For the finance team, the portal will feature a Fraud Detection dashboard:
○ A list/table of processed financial documents (invoices, receipts) with a status: OK or Flagged. They can filter to see only flagged ones.
○ Clicking an entry shows details: e.g., the receipt image or data, and Fang’s analysis (“Flagged because amount too high and unapproved vendor”). It might highlight the suspect values in red.
○ The finance user can then mark the alert as “reviewed” and add a note (like “Confirmed legitimate” or “Investigating further”). This feedback is logged and can be used by us to adjust Fang’s criteria if needed.
○ We’ll also allow a bulk upload or scan feature on this page, so they can drag in a bunch of receipt files and have Fang process them with results popping up live.
○ Given fraud detection can be sensitive, we’ll ensure this UI is only accessible to authorized finance personnel (and possibly senior management for oversight).
○ The design will use clear icons/indicators (e.g., a red warning icon for flagged, green check for cleared) to make it very clear where attention is needed.
● User Testing and UX Considerations: During implementation, we will involve representative end-users to test these interfaces. For example, we might have Bill or his team use the Riddler chat in staging with real questions to see if the answers make sense and if the UI displays them in a helpful way (e.g., maybe we need to show the answer and a collapsible “sources” section). Similarly, an estimator will test the Bid upload to ensure the form fields and flow match their needs. We will iterate on UI/UX with feedback – perhaps adding tooltips, help modals (like an explanation of what each agent does for new users), etc., to make adoption easier.
● Design Aesthetic: The UI will be modern and lightweight. We’ll likely use a component library like Material-UI or similar for a professional look (consistent with enterprise apps). Color-coding might be used (for example, using Acme’s brand colors, or coloring agent names: maybe red for urgent things, green for OK, etc.). We’ll also ensure responsiveness (so it works on tablets or different screen sizes if needed, as perhaps field managers might open it on an iPad).
● Interface for Testing Agents: In addition to production use, we can incorporate an Admin/Testing mode (likely for the project champions or admins only) where they can directly interact with an agent’s prompt. This could be a page where, say, you pick an agent from a dropdown, input some test text, and see the raw output from the LLM. This is mainly for debugging and reassurance during development – for example, Bill might be curious exactly how SIFT is categorizing something, and this test console lets him experiment. It’s not a typical end-user feature, but providing it (even if hidden) can help build transparency and trust with Acme’s team during rollout.
In summary, the UI is designed to make the AI agents’ presence feel natural in Acme’s workflows. Rather than forcing users to learn a complicated new system, it augments what they already do: it triages emails but delivers them in familiar inboxes with labels, it parses bids and presents them in a format similar to their existing bid tracking, it answers questions so they don’t have to open dozens of files, and it flags finance issues before they become problems. The emphasis is on clarity, user control (ability to review/override AI decisions), and seamless integration – all of which will drive user adoption and confidence.
7. Training, Support, and Feedback Loops
Introducing AI agents into Acme’s operations requires careful training for users, ongoing support, and mechanisms to incorporate feedback into the system. We are committed to a high-touch enablement approach to ensure Acme’s team is comfortable and confident with the new tools, ultimately driving successful adoption. Below are the plans for training, support, and continuous improvement:
● User Training Program: We will deliver a comprehensive training program starting alongside the initial agent deployments (as indicated in the timeline around August 2025). This includes:
○ Live Training Sessions: We’ll conduct interactive training workshops (via Zoom or on-site if feasible) for each agent or group of related agents. For example, a session for the Estimating team on using Bid Centry and Launch Packages, a session for the Admin/Operations team on SIFT & Triage email console, and one for Finance on Fang. During these sessions, we’ll demonstrate the UI, process a few real examples, and then let users try it themselves with guidance. According to our agreement, we have flexible training hours (approx. 200 hours allocated) plus unlimited chat support, which means we can do multiple sessions and follow-ups as needed.
○ Training Materials & Documentation: We will provide user-friendly documentation, including quick start guides, FAQs, and step-by-step walkthroughs. For instance, a one-pager cheat-sheet for “How to ask questions to Riddler effectively” or “What to do when SIFT flags an email.” We’ll also supply video screencasts for common tasks, so users can refer back anytime. All these enablement materials – implementation guides, usage docs, onboarding decks – will be compiled and shared with Acme’s team.
○ Onboarding New Users: As new employees join Acme or new teams start using the system, we will help onboard them. This might include periodic training sessions every few months for new hires or train-the-trainer approach where certain Acme staff become internal champions who can train others.
● Dedicated Support Channels: Jeeva AI will provide ongoing support to Acme to ensure any issues or questions are resolved promptly:
○ Customer Success Manager (CSM): As part of the agreement, a dedicated CSM (Customer Success Manager) will be assigned to Acme. This person’s role is to maintain regular contact, understand Acme’s evolving needs, and ensure the delivered solution continues to provide value. The CSM will schedule regular check-ins (for example, weekly during initial rollout, then monthly strategic reviews). In these meetings, we’ll review system usage, address any concerns, and plan any adjustments or new feature requests. The CSM acts as Acme’s advocate within Jeeva, coordinating engineers to address feedback quickly.
○ 24/7 Priority Support: Acme will have access to round-the-clock support for critical issues. We will set up a support email and a chat channel (like Slack or MS Teams channel) that Acme’s authorized users can reach out to anytime. For urgent issues (system down, major functionality broken), our team is on call to respond immediately (with defined SLA response times, e.g., within 1 hour for critical issues). Additionally, phone support is available for high-priority incidents as needed.
○ Issue Tracking: We’ll likely use a ticketing system (like Jira or Zendesk) to track any bugs or feature requests reported by Acme’s team. The company’s users can report issues via email or the chat, and we’ll log them, prioritize, and keep the reporters updated on resolution progress. This ensures nothing falls through the cracks and Acme can see transparency in how quickly things are fixed.
● Feedback Loops and Continuous Improvement: We recognize that the first version of any AI system will have areas to refine. We plan formal and informal feedback loops:
○ Regular Feedback Sessions: In addition to the CSM check-ins, we will host a post-launch review at key milestones (for example, end of August after initial live usage, and end of September after advanced agents plan, etc.). In these sessions, we’ll gather structured feedback: What is each agent’s usefulness? Any frustrations or ideas from users? This could be done through surveys and live discussions. As per our timeline, September is specifically a feedback-driven development period.
○ In-App Feedback Mechanisms: The UI itself will have quick feedback options. For instance, after Riddler answers a question, the user can thumb-up/down it – this data will be sent to us for review (and potentially fine-tuning prompts). On the email triage screen, if a user reclassifies an email that was misclassified, the system will log that and we can use it to update SIFT’s prompt or add an example to reduce future errors. We might also include a “Feedback” button on each page where users can jot a quick note (“The output here was confusing because ...”).
○ Prompt and Model Tuning: Based on feedback and observed outputs, we will refine the prompts or switch models if needed. For example, if users report that Riddler sometimes gives too verbose answers when they just needed a number, we can adjust that prompt. Or if Fang misses some tricky pattern, we might add new rules or examples to its logic. Because our CI/CD pipeline allows frequent updates, we can iterate quickly – minor prompt tweaks could be deployed within a day after observing an issue. More significant changes (like adding a new data source to Riddler because users want a different kind of query) can be scheduled into the next sprint.
○ Continuous Learning (no model retraining, but improvement): Although we are not retraining the base models, the system “learns” in other ways: via accumulating knowledge in the database (e.g., as more documents and their AI-extracted metadata are stored, Riddler can answer more questions), and via prompt adjustments. We might also maintain a growing set of Acme-specific examples that we include in certain prompts to improve accuracy (effectively fine-tuning via prompt engineering). The feedback loop helps identify what examples to include. For instance, if SIFT consistently struggles with a particular type of email (say, distinguishing a subcontractor’s weekly report from a normal email), we can add an example of that to the prompt.
● Phase-wise Hand-holding: During the initial phases of rollout, we’ll be closely monitoring and assisting:
○ In August’s live usage, we’ll likely have someone from Jeeva (or the CSM) practically on standby during business hours as users first engage with the system in production, to answer “How do I do X?” or “This looked weird; is that expected?” quickly. This builds user trust, as they know support is right there.
○ We may also identify “internal champions” within Acme’s team (people who are early adopters or tech-savvy) and give them extra training so they can help their peers day-to-day. These champions can funnel consolidated feedback to us as well.
● Ongoing Partnership: Our goal is not just to deliver and walk away, but to form a partnership where Jeeva AI continuously helps Acme derive value from the system. As such, beyond October 2025, we will remain engaged:
○ We will provide strategic alignment meetings (perhaps quarterly) to discuss new opportunities to use AI in Acme’s operations or any upcoming challenges (for example, if Acme enters a new region or business line, how can the agents adjust).
○ The contract includes custom enhancements on demand, meaning if Acme requests new features or tweaks, we will prioritize those (within scope or via change control if major). For instance, Acme might say, “We also want an agent to help with OSHA compliance documentation.” The team would then collaboratively scope that out and potentially start working on it as a phase 2.
○ Unlimited user access and our scalable platform means if Acme’s usage grows (more users, more data), we will be there to ensure the system scales and everyone is supported.
● Measuring Success and Adoption: We will define some KPIs to monitor adoption: e.g., number of queries asked to Riddler per week, number of emails auto-triaged vs manual, time saved in bid processing. The CSM will share these metrics with Acme’s leadership to show progress. If some areas show low usage, that’s a flag for us to investigate why – maybe users need more training or the feature needs improvement. This data-driven approach to feedback ensures we focus efforts where the value isn’t fully realized yet.
By implementing thorough training and maintaining a tight feedback loop, we aim to make Acme’s team not just users but enthusiastic proponents of the AI agents. Early support builds trust, and continuous improvement shows we listen. Ultimately, this collaborative approach de-risks the project (any issues are caught and fixed early with user input) and drives higher satisfaction, which is key to the long-term success (and ROI) of the solution.
8. Security, Compliance, and Privacy Considerations
Acme understandably places heavy emphasis on data security, AI safety, and business continuity. Our implementation plan has been engineered from the ground up to address these concerns proactively. Below we outline how we will handle security, compliance, and privacy to meet or exceed Acme’s requirements:
● Data Locality & Ownership: All of Acme’s data will remain within Acme’s controlled environment. The solution is deployed on Acme’s dedicated AWS infrastructure (or an isolated cloud environment for Acme) ensuring data isn’t co-mingled with any other organization. We explicitly guarantee that Acme’s data will not be used to train any external AI models. The data is only used at runtime to get outputs for Acme and is not stored or seen by OpenAI/Anthropic beyond the immediate API call (which, as noted, does not retain it). Acme retains full ownership of all their data and any derivative data (like processed outputs, embeddings, etc.). If the contract ends, Acme can request deletion or handover of all data from our systems.
● AI Model Safety & Reliability: We will implement measures to ensure the AI agents behave safely and as expected:
○ Prompt Safeguards: Each prompt will include instructions to the AI to avoid certain pitfalls (e.g., not to disclose confidential info to unauthorized queries, not to produce harassing or inappropriate content – though this is mostly an internal system, it’s still good practice). For example, Riddler’s prompt will instruct: “If the user asks for information they are not permitted to see, respond with an apology and that you cannot provide that.” We also use OpenAI’s built-in content filters for moderation when appropriate (OpenAI’s API can flag if an output might contain sensitive content, though in our use case this is unlikely).
○ No Autonomous Actions without Approval: Even though these are “agents”, we constrain their actions to safe bounds. For instance, Launch Packages might draft an email but not send it without human review. No agent will delete or alter existing data irreversibly without a checkpoint. File Sweeper won’t purge files; it will mark for archive and maybe notify an admin who can approve deletion. This human-in-the-loop design prevents accidents, especially early on while confidence is building.
○ Testing in Sandbox: We will thoroughly test the agents in a sandbox with real-like data before enabling them on live data, to observe and correct any unexpected behavior. This includes simulating worst-case scenarios (e.g., confusing inputs) to ensure the agent responses are still safe and reasonable.
○ Failsafe Mechanisms: If an agent encounters a situation, it isn’t sure about, it is designed to fail safe – e.g., instead of giving a possibly wrong answer or taking a wrong action, it will escalate to a human. Riddler might say “I’m sorry, I cannot find that information” rather than hallucinate an answer if it’s uncertain. Fang might flag “possible issue” rather than giving a clean pass if it isn’t fully confident.
● Compliance (Industry and Legal): The industry may involve compliance in areas like safety regulations, environmental laws, etc. While these mostly pertain to operations, our system can assist in compliance by tracking related documents and actions (e.g., ensuring safety meeting transcripts are saved, permits are up to date). We will comply with data protection regulations: though Acme’s data is mostly internal business data, any personal data (like employee info in HR emails) will be protected under policies akin to GDPR/CCPA. This means:
○ If there’s personal data, we won’t use it for anything beyond the intended purpose (no personal profiling).
○ Users can request removal of personal data (we can design the system to delete or anonymize certain data if needed).
○ Our team will sign NDAs and the SaaS agreement likely covers confidentiality to ensure we as providers handle Acme’s data with care.
○ If Acme has to comply with specific standards (like SOC2, ISO27001) through their clients, our platform and processes (being backed by OpenAI’s and AWS’s compliance in many areas) will help support that. We can provide documentation on how data is stored and secured for their auditors if needed.
● Encryption & Network Security: As detailed in Architecture, everything is encrypted in transit (TLS) and at rest (AES-256). We use strong ciphers and follow AWS security best practices. Additionally, we will enforce secure authentication (complex passwords, possibility of 2FA if required via their SSO). Network security includes VPC isolation and IP whitelisting for any remote DB access if applicable. Only the application servers can query the database – e.g., even developers cannot directly access prod data without going through bastion and audit (and we’ll minimize that too).
● Role-Based Access & Data Partitioning: We touched on RBAC in Architecture – here’s how it ensures privacy:
○ Each user only sees data relevant to their role. For instance, a project engineer might use Riddler to ask about project timelines but wouldn’t be able to retrieve financial data, because Riddler will internally filter out finance-related documents for that user’s queries. If they somehow try a direct question, Riddler will respond with no data or a polite refusal, not exposing unauthorized info.
○ Within the database, we can tag data with access levels. E.g., mark certain documents as “Finance-confidential”. The retrieval system will check the user’s role tag before including that in a query result.
○ Admins will have oversight – an Admin user (with high privileges) might have a special audit console to see who queried what on Riddler, etc., which aids internal compliance and deters misuse.
● Audit and Monitoring for Security: We will monitor the system for any unusual activity. For example:
○ If there are repeated failed login attempts (potential brute force), the system can lock out or alert admins.
○ We’ll use AWS CloudTrail to log access to AWS resources. If someone tries to access the database outside the app, it’ll be logged and alarmed.
○ The application itself can include intrusion detection logic – e.g., if a user is suddenly querying a lot of data they never accessed before, we might flag it for review (this could be a sign of a compromised account). This is an advanced idea, but we can implement simple thresholds or at least highlight usage patterns to the CSM.
○ The combination of logs and monitoring means any security incident can be traced and responded to quickly.
● Business Continuity & Disaster Recovery: Ensuring the system is always available and that Acme’s operations aren’t disrupted is a top priority:
○ High Availability: As noted, multi-AZ deployments and stateless scaling ensure high uptime. We aim for a very high uptime (our target can be 99.5% or higher) which means minimal downtime. Maintenance can be done with zero downtime by rolling deployments.
○ Backups: The database will have daily backups and point-in-time recovery enabled. S3 data is inherently redundant; we might also periodically export critical data to a secondary storage (maybe an offline backup on request).
○ Disaster Recovery Plan: We will document and test recovery procedures. For example, if AWS region goes down, can we spin up in another region? Possibly we’ll keep Terraform scripts ready to deploy to a secondary region if needed. Or at least have data backups that can be restored in another region. Given the short timeline we may not implement multi-region active-active, but it’s something we’ll design for future if Acme requires. At minimum, the RTO (time to recover) in a total failure scenario (like region outage or a severe bug) might be in the range of a few hours to restore service elsewhere.
○ We’ll also plan for less catastrophic continuity: e.g., if OpenAI API has an outage or is slow, the system should degrade gracefully (maybe Riddler says “sorry, I’m temporarily unavailable” rather than hanging, and an alert is sent to us to switch to a backup model provider if possible). We can provision an alternative LLM (like Anthropic or an on-prem model) to use as a fallback if one fails.
● Privacy of Sensitive Content: Some data like financial records or HR emails are sensitive. Apart from access controls, we ensure that even within the system, such data is handled carefully:
○ If we use any third-party service (like OpenAI), we won’t send highly sensitive personal identifiers unless absolutely necessary. For instance, Riddler answering an HR question might be better handled by limiting to an internal database query if it involves personal info, rather than open-ended LLM call.
○ We will also honor any data retention policies: if Acme wants certain data to auto-delete after X years (say old emails or archived files after 7 years), we can configure the system to do that (S3 lifecycle policies, etc.). This is sometimes needed for privacy compliance.
○ User Privacy: If employees use the system to ask questions or the system monitors emails, we ensure it’s in line with company policies. It sounds like this is an internal system so employee consent is implicit through company IT policy, but we can help by making the system’s actions transparent (it’s clear that the AI is reading their emails to categorize, etc., nothing hidden).
● Compliance Standards and Audit Support: Jeeva AI’s platform, being backed by notable investors and presumably having other enterprise clients, likely aligns with industry best practices (the mention of Sam Altman/Benioff backing implies we follow high standards). We can provide Acme with details on our internal security practices if needed (like that we passed a SOC2 audit, etc., if applicable, since OpenAI has SOC2 and we align with that). If Acme’s clients ever raise concerns about AI usage, we can furnish a security whitepaper and be available to answer those queries, making sure Acme can confidently justify the system’s security to their stakeholders.
● Ethical AI and Bias: Although not explicitly asked, worth noting: we will ensure the AI’s decisions are fair and based on relevant data. For example, if Riddler is asked about employee performance (if that ever came up), it won’t make judgments beyond data. Or Fang’s fraud detection will be based on behavior, not on any protected attribute of an employee. Essentially, we will avoid building in any bias (not that this use case has obvious bias concerns, but it’s part of AI safety ethos). And if any AI output could be sensitive (like summarizing an email where tone could be misinterpreted), we encourage a human review stage.
In essence, our approach is to embed security and privacy into every layer of the system – from infrastructure to application logic to user-facing features – and to have clear processes for maintenance and incident handling. This comprehensive strategy directly addresses Acme’s key concerns (Bill explicitly emphasized data staying local/private, which we have covered, and wanting safe usage, which we ensure through testing and guardrails). By implementing these measures, Acme’s team and their leadership can have full confidence that while the AI agents are accelerating their workflows, they are not introducing undue risk. We are happy to undergo any security reviews or testing Acme requires (penetration testing, etc.) as part of the deployment process to validate these protections.
9. Outcome-Driven Value Realization
The success of this project will ultimately be measured by the tangible benefits it delivers to Acme’s . By automating and augmenting key workflows, the AI agents are expected to save time, reduce errors, and improve operational margins. Here we outline how each part of the solution translates into concrete value, and how Acme can realize and measure these outcomes:
● Significant Time Savings: Many of the tasks targeted by our agents are currently manual and time-consuming. By offloading them to AI:
○ Email Triage (SIFT/Triage): Instead of staff spending hours sifting through an inbox of mixed-priority emails, important emails will surface immediately. For example, if Acme receives 100 emails a day and 20 are high-priority, SIFT ensures those 20 are identified in seconds. This could easily save an employee 1-2 hours per day of sorting and categorizing, not to mention quicker responses to urgent matters. Over a year, that’s hundreds of hours saved, which can be redirected to more value-added work (like actually responding to clients or solving on-site issues).
○ Bid Processing (Bid Centry): Rather than an estimator reading a 50-page RFP line by line to pull out dates and requirements – which could take several hours – the agent can do it in minutes. This accelerates the go/no-go and bidding preparation process. If Acme can process bids faster, they might be able to bid on more projects or spend more time crafting a winning proposal rather than clerical data extraction.
○ File Organization (File/Chrono Sweeper): Employees currently might waste time looking for documents or organizing files. With automatic organization and powerful search (via Riddler), finding information becomes much quicker. A task like finding “the meeting minutes where X was discussed” might drop from an hour of digging to a 30-second query. The time savings from reduced information search are notoriously high in organizations – some studies show knowledge workers spend 20-30% of their time searching for information. Even cutting that in half would be a substantial productivity boost.
○ Launch Packages: Starting a new project typically involves coordinating many tasks – possibly taking a project manager several days of back-and-forth to ensure everything’s set up. Launch Packages can compress a lot of that into a one-day or even same-day process. This means projects can start sooner, and project managers have time freed to focus on planning and execution details rather than administrative setup.
○ Fang (Fraud Detection): Auditing expenses manually or after the fact can be slow. Fang catches issues almost in real-time, saving the finance team time in audits and potentially saving lengthy investigations down the road. Also, by automating initial checks, finance staff can focus on the flagged issues rather than scanning every single entry, improving efficiency.
● Error Reduction and Improved Accuracy: Humans are prone to oversight, especially when dealing with tedious tasks. The AI agents will operate with consistency:
○ No missed emails or deadlines: SIFT and Triage dramatically reduce the risk of a critical email being overlooked or a bid deadline being missed. This can prevent costly errors like failing to submit a bid on time (which could mean lost revenue opportunities) or not responding promptly to a client (which could harm relationships).
○ Data accuracy: Bid Centry will extract data without the typos or copy-paste errors that a human might introduce when rushing. This means Acme’s records (bid dates, client info) will be more accurate, which prevents downstream mistakes (like using wrong dates when scheduling work or misquoting something).
○ Consistent workflows: Launch Packages ensures every required step for project initiation is listed, so nothing falls through the cracks (permits, safety plans, etc.). This reduces the risk of compliance issues or last-minute scrambles because a step was forgotten.
○ Fraud and anomaly detection: By catching fraudulent or incorrect charges that humans might miss (especially if someone is maliciously trying to hide them), Fang can prevent financial leakage. Stopping a single significant fraudulent invoice (say $10,000) that would have been paid by accident essentially directly adds that amount to the bottom line that would’ve been lost.
● Faster Decision Making and Insights: Riddler, the central intelligence, transforms how employees get information. Instead of days of compiling reports or analyzing spreadsheets, they can ask and receive answers in seconds:
○ For instance, a question like “What’s our total bid pipeline for next quarter?” might require an analyst to gather data. Riddler can answer on the fly by aggregating bid data. Quick insights mean leadership can make decisions faster (like adjusting resource allocation if pipeline is low/high).
○ Another example: spotting “price creep” from subcontractors – Riddler can quickly compare initial quoted prices vs final invoices across projects. This insight could allow Acme to renegotiate or choose different subcontractors, directly saving cost.
○ In general, democratizing data access through Riddler means employees spend less time waiting on reports from others. They can self-serve answers to inform their actions, which speeds up operations and encourages data-driven decisions at all levels.
● Scalability of Operations Without Linear Cost Increase: With these automations, Acme can handle more volume without proportionally increasing headcount:
○ If the company grows (more projects, more bids, more emails), the AI agents simply scale up (we add more compute or a new model instance) and continue handling the load. Acme’s team can remain lean. For example, perhaps normally 2-3 more administrative staff would be needed to handle an extra 50% increase in projects for filing, emailing, etc. With our system, Acme might absorb that growth with the same staff, improving their operations margin (revenue per employee goes up).
○ This directly impacts ops margin: doing more with the same or fewer resources. We can attempt to quantify: Say Acme does $X million in projects with Y staff today. If after the AI rollout, they can increase throughput by 20% without hiring, that extra revenue is achieved at a much higher margin (since fixed costs didn’t rise as much).
○ Additionally, reducing errors and rework saves money – e.g., not having to re-do a bid because of an initial oversight, or avoiding a fine because a permit wasn’t missed, all preserve profit.
● Qualitative Benefits (Harder to measure but important):
○ Employee Satisfaction: Taking away drudgery (like shuffling papers, data entry, inbox overload) means employees can focus on more engaging tasks (like strategy, client communication, actual project management). This improves job satisfaction and can reduce burnout – especially important for key people like project managers or estimators who may be overburdened with admin tasks. Happier employees often means better retention, which has its own cost savings (less turnover).
○ Client Satisfaction: Faster responses and fewer mistakes will be felt by Acme’s clients and partners. For instance, clients get prompt answers (because emails were routed swiftly), and projects start on time and run smoother (because nothing was missed at kickoff). Over time, this can improve Acme’s reputation, potentially leading to more business (though intangible, it’s significant).
○ Transparency and Control: The system will provide management with better oversight – e.g., via Riddler or dashboards, management can easily check status of bids, communications, etc. This transparency means management can catch issues early (maybe noticing “hey, we’re seeing a lot of high priority issues flagged this week, let’s allocate more resources to that project”). Essentially, it’s like giving managers a live pulse of the company’s operations, which is valuable for proactive management.
● ROI and Cost Justification: The proposal is ~$250K for the first year. We can outline how the benefits outweigh this:
○ Labor savings: If the system saves, say, 2-3 FTE worth of labor (through automation and efficiency), and an FTE costs maybe $80k fully burdened, that’s $160-240k/year saved or reallocated to higher value tasks – almost covering the cost alone. And that’s a conservative estimate considering the wide range of tasks affected.
○ Error avoidance: Avoiding one missed big project (a bid not submitted or a lost client due to slow response) could cost far more than $250k in lost revenue. The system acts as insurance against such costly misses.
○ Fraud prevention: If Fang catches even a single major fraudulent activity or costly accounting error (which could be tens of thousands), that’s immediate ROI. Fraud can be sneaky; having an AI watchdog is like adding a safeguard that could pay for itself the first time it catches something.
○ Accelerated revenue: Launching projects faster or bidding more could directly increase revenue. If Acme can bid on, say, 5 more projects a year because of efficiency, and wins 1 with profit >$250k, it already pays off.
○ We will work with Acme to define key metrics to track these outcomes (like time to respond to high-priority email, number of bids handled per estimator, number of compliance issues prevented, etc.). This way, over the year, we can quantitatively demonstrate improvements.
● Growth Enablement: Beyond immediate savings, the AI platform sets Acme up for scalable growth. As Acme expands (geographically or in project volume), they won’t necessarily need to expand their back-office at the same rate. That means higher throughput and capacity with minimal incremental cost. So the margin on each additional project improves. We essentially flatten the cost curve of growth for many support functions.
● Examples of Outcome Scenarios: It might be useful to illustrate a few “day in the life after AI” scenarios:
○ Before vs After Email Triage: Before, an employee might spend each morning reading 50 emails and manually prioritizing – possibly missing one important item until later. After, when they log in at 8 AM, the AI has already sorted and highlighted the 5 critical emails that arrived, with suggested responses or routing already in motion. They address those first and look like a hero to their clients/boss for being so on top of things.
○ Bid Preparation: Before, preparing the internal summary of a new RFP took half a day and was prone to errors if rushed. After, within 10 minutes of receiving the RFP, the team has a summary and can have a go/no-go meeting immediately with accurate info. They can start working on the proposal earlier, possibly making a better submission and increasing win rate. More wins = more revenue.
○ Project Launch: Before, starting a project was chaotic, maybe something gets forgotten and causes a delay later (like “Oh, we forgot to get a utility disconnect scheduled, now we have to pause for a week”). After, everything is laid out and done before ground break, avoiding such costly delays. Smoother projects could mean finishing faster, freeing capacity to take on more projects in a year or avoid overtime costs.
○ Finance integrity: Before, if a fraudulent invoice went through, Acme might only catch it in an annual audit (if ever), by which time money is gone. After, Fang flags it right away, they stop payment – directly saving money and sending a message that such attempts will be caught.
By implementing this system, Acme’s is effectively gaining a virtual “team” of tireless assistants that elevate the performance of the human team. The outcome is a more efficient, error-resilient, and agile operation, which translates to cost savings and higher profit margins. We anticipate that within the first year, Acme will see clear evidence of these benefits – likely in the form of faster project cycles, improved bid win rates, fewer fire-drills on missed items, and dollars saved from prevented issues – making the ROI not just theoretical but demonstrably real.
Our plan is to continuously track these outcomes and adjust to maximize them. We succeed when Acme’s leadership can clearly say: “This AI investment has paid for itself and then some, and we have grown our business without growing our headaches.” That is the ultimate measure of closing this $250K deal – that it delivers far more than $250K in value back to Acme’s .
10. Code & Infrastructure Details
To give a flavor of the implementation and ensure our engineering teams are unblocked, this section provides some specific code snippets and configurations relevant to the project. These examples illustrate how we will implement key components like the FastAPI endpoints, database schemas, and agent logic using our stack.
10.1 FastAPI Endpoint Example – File Upload (Bid Centry)
We will use FastAPI for our backend API. Here’s a simplified example of an endpoint to handle file uploads for the Bid Centry agent. This endpoint accepts a bid document file, processes it, and returns the extracted data:
Explanation: In this snippet, extract_text_from_bytes would be a function that handles PDF/DOCX extraction (possibly using an external library or AWS Textract). bid_centry_agent.parse_bid_text(text) encapsulates the LangChain prompt to GPT-O3 that returns a dictionary of the extracted fields. We then save the result in a MongoDB collection (bids). The response_model BidExtractedData ensures FastAPI will return JSON in a structured way. This endpoint will be used by the React frontend when a user uploads a bid document. Error handling is in place to catch unsupported files or processing errors.
10.2 MongoDB Schema Example – Email and Routing Records
We will use MongoDB to store processed information. We don’t strictly enforce schemas in Mongo, but using Pydantic models helps maintain consistency. Here’s an example of how we might structure the data for emails triaged by SIFT and routed by Triage:
In use, after SIFT and Triage run, we’d populate such a record. This makes it easy to track later what happened to each email (an audit trail of triage). If an email was re-routed manually, we could update routed_to or add a comment. Over time, this collection can be queried to evaluate SIFT/Triage performance (e.g., “how many emails got priority High each week”).
We might also have collections for other agent outputs:
● bids collection for Bid Centry outputs (fields as in the Pydantic model above, plus maybe status – whether bid was submitted/won, etc.).
● files collection indexing files by tags and metadata (from File Sweeper).
● alerts collection for Fang’s fraud alerts (storing details of each alert and resolution status).
● projects collection for Launch Packages (storing generated checklist items, etc., and their completion status).
10.3 LangChain Agent Handler Template
We leverage LangChain to implement agent logic. Let’s show a pseudo-code for a LangChain chain, for example the SIFT email classifier:
This outlines how we set up an LLMChain with a prompt. The classify_email function can be integrated into the SIFT agent code. After classification, the rest of SIFT’s code would create the email record and call Triage.
For a more tool-oriented agent (like Riddler), LangChain’s Agent with Tools would be used:
● We might define a DatabaseSearchTool that, given a query, returns relevant docs from Mongo (or a vector store).
● Riddler’s agent would then have the LLM plan using tools: e.g., it might decide to call the search tool, get some data, then formulate an answer.
● LangChain provides a framework for this (ReAct pattern or ConversationalRetrievalChain for Q&A with context). We’ll likely implement Riddler as a Retrieval QA chain with context from vector store, as that’s straightforward and deterministic in process.
Pseudo-code for Riddler using retrieval:
Here, the user_role could be used inside our retrieval logic to filter sources (not built-in to RetrievalQA, but we can subclass the retriever to respect roles). The answer and sources would then be returned to the API endpoint to display to user.
10.4 Terraform Snippet – ECS Task and Security Group
Expanding on infrastructure as code, here’s a snippet that might define the ECS task and security group for the FastAPI service:
And a security group to allow only the LB to talk to the service and service to DB:
These Terraform resources ensure our FastAPI service is deployed on Fargate with the right networking. The environment variables feed in secrets (which would be provided via Terraform variables or AWS Secrets Manager integration). We’d similarly define resources for S3, DocumentDB etc., as mentioned.
10.5 Putting It Together – Agent Orchestration Example
Finally, an integrated snippet to show how an agent might be orchestrated end-to-end (combining FastAPI, LangChain, and DB). Let’s use a simplified Riddler endpoint example:
In this pseudo-code, riddler_agent.answer_query would internally use something like the RetrievalQA chain described earlier, but tweaked to apply role filtering. We log each Q&A to a riddler_audit collection with timestamp and user info. This not only provides traceability but can also be used to improve answers over time (seeing what people ask and if the answers were satisfactory).
The above code snippets are illustrative but aligned with Jeeva’s stack and best practices. By reviewing them, the engineering team on both sides (Jeeva and Acme’s IT) can understand how components will be implemented. We will maintain a repository with a clear structure, e.g.:
Everything will be documented so any developer can jump in. With this level of detail and preparation, we are set for an execution-ready implementation. The code and infrastructure pieces will evolve with feedback and further testing, but the foundation is laid out clearly to drive development forward quickly and safely.
Sources: The plan leverages information from meeting notes and the partnership proposal to ensure alignment with Acme’s needs (e.g., agent definitions, security requirements, and timeline milestones). These source materials guided the scope and priorities, ensuring the proposal is realistic and tailored for Acme. Each aspect of the plan – from architecture to training – is crafted to instill confidence that Jeeva AI will deliver a safe, fast, and secure agent solution that drives measurable value and successfully justifies Acme’s investment.
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