Post-sign-in landing page

Problem Statement Government agencies waste billions annually on:

Duplicate contracts: Multiple agencies buying the same services without coordination Weak KPIs: Vague metrics like "improve satisfaction" without measurable targets Scope creep: Open-ended language leading to budget overruns Missing elements: Lack of acceptance criteria, assumptions, or success metrics Solution An AI-powered web application that analyzes SOW documents to:

Extract structured data (tasks, KPIs, deliverables, metadata) Identify weak or unmeasurable KPIs Flag scope creep language and red flags Detect missing critical elements Generate SMART KPI alternatives (Phase 3) Compare across contracts for duplication (Phase 4) Architecture ┌─────────────────┐ ┌─────────────────┐ │ Next.js │ HTTPS │ FastAPI │ │ Frontend │ ◄─────► │ Backend │ │ │ │ (Railway) │ └─────────────────┘ └────────┬────────┘ │ ▼ ┌─────────────────┐ │ Claude API │ │ (Anthropic) │ └─────────────────┘ Tech Stack Backend:

Python 3.12 FastAPI for REST API Anthropic Claude API (Haiku model) PyMuPDF (PDF parsing) python-docx (DOCX parsing) Frontend:

Next.js 15 React 18 TypeScript Tailwind CSS Axios for API calls Deployment:

Backend: Railway (https://railway.app) Features Current (MVP) File Upload: Drag-and-drop interface for PDF, DOCX, TXT files Data Extraction: Automatically extracts: Contract metadata (ID, contractor, value, dates) Objectives and tasks KPIs and deliverables Scope and personnel requirements Risk Analysis: Identifies: Weak KPIs (missing targets, baselines, timelines) Scope creep language Missing critical elements Red flags and inconsistencies Beautiful Dashboard: Color-coded severity levels, downloadable results Coming Soon SMART KPI Generator: AI-generated specific, measurable alternatives Cross-Contract Analysis: Detect duplicate or overlapping work Batch Processing: Analyze multiple SOWs at once Historical Comparison: Track improvements over time Project Structure calhacks/ ├── main.py # FastAPI backend server ├── sow_extractor.py # Data extraction ├── risk_analyzer.py # Risk analysis ├── rag_analyzer.py # RAG-based overlap detection ├── overlap_analyzer.py # Overlap detection logic ├── vector_db_setup.py # Vector database initialization ├── annotated_examples.json # Training data for RAG ├── requirements.txt # Python dependencies ├── railway.json # Railway deployment config ├── sample_nyserda_sow.txt # Sample SOW for testing │ ├── frontend/ # Next.js frontend │ ├── app/ │ │ ├── page.tsx # Main page │ │ ├── layout.tsx # Root layout │ │ └── globals.css # Global styles │ ├── components/ │ │ ├── FileUpload.tsx # Upload interface │ │ ├── ResultsDashboard.tsx # Results display │ │ └── LoginModal.tsx # Login interface │ ├── package.json # Node dependencies │ └── next.config.js # Next.js configuration │ └── chroma_db/ # Vector database storage Local Development Prerequisites Python 3.12+ Node.js 18+ Anthropic API key (Get one here) Backend Setup

Install dependencies

pip install -r requirements.txt

Create .env file

echo "ANTHROPIC_API_KEY=your-key-here" > .env

Initialize vector database

python vector_db_setup.py

Run server

python -m uvicorn main:app --reload --host 0.0.0.0 --port 8000 Backend will be available at: http://localhost:8000

Frontend Setup cd frontend

Install dependencies

npm install

Run development server

npm run dev Frontend will be available at: http://localhost:3000

Deployment See DEPLOYMENT.md for detailed deployment instructions.

Quick Summary:

Deploy Backend to Railway

Push to GitHub Connect repo to Railway Add ANTHROPIC_API_KEY environment variable Get backend URL

Connect GitHub repo Set NEXT_PUBLIC_API_URL to Railway backend URL Deploy Cost Analysis Service Free Tier Usage Railway $5/month credit Backend hosting Claude API Pay per use ~$0.02-0.05 per analysis Total: Free for <100 analyses/month

Testing Test with Sample SOW Upload the included sample_nyserda_sow.txt file through the UI or test API directly:

curl -X POST http://localhost:8000/api/analyze \ -F "file=@sample_nyserda_sow.txt" Expected Results The analyzer should find:

14+ issues total Weak KPIs like "reduce processing time to target levels" Scope creep: "ongoing support as needed" Missing elements: specific acceptance criteria How It Works Pass 1: Extraction Uses Claude API to extract structured data from raw SOW text Identifies: metadata, tasks, KPIs, deliverables, scope Returns: JSON with all extracted fields Pass 2: Risk Analysis Analyzes extracted data for issues Categories: weak KPIs, scope creep, missing elements, red flags Assigns severity: HIGH, MEDIUM, LOW Pass 3: Enhancement (Coming Soon) Generates SMART alternatives for weak KPIs Provides specific, measurable, achievable recommendations Pass 4: Overlap Detection (Coming Soon) Compares across multiple SOWs Identifies duplicate or overlapping work Calculates potential savings API Documentation POST /api/analyze Upload and analyze a SOW document.

Request:

POST /api/analyze Content-Type: multipart/form-data

file: Response:

{ "success": true, "filename": "contract.pdf", "contract_id": "SOW-2024-001", "contractor": "Example Corp", "summary": { "total_findings": 14, "high_severity": 3, "medium_severity": 7, "low_severity": 4, "tasks_found": 8, "kpis_found": 12 }, "extracted_data": { ... }, "analysis": { "weak_kpis": [...], "scope_creep": [...], "missing_elements": [...], "red_flags": [...] } } GET / Health check endpoint.

Response:

{ "status": "online", "service": "SOW Analyzer API", "version": "1.0.0" } Contributing This is a hackathon project. Contributions welcome!

Fork the repository Create a feature branch Make your changes Submit a pull request License MIT License - see LICENSE file for details

Acknowledgments Built using assistance from Claude AI by Anthropic for certain analysis function(s) Frontend powered by Next.js Backend powered by FastAPI Deployed on Railway