ClauseIQ
Budżet: $150.0
FIXED /
⭐ 0.00 (0)
Canada
artificial-intelligence, machine-learning, data-science, deep-learning, react-js, node.js, mongodb, sql, python, mean-js, css3, itsm, html5, c, android-studio, xml, javascript, java, c++
Project Overview
### Purpose
ClauseIQ is a demonstration of enterprise-grade AI engineering, built to be deployed as a portfolio project on freelance platforms and as a foundation for a potential SaaS product.
### Scope
**In Scope:**
- PDF contract upload and processing
- AI-powered clause extraction, classification, and risk scoring
- Question-answering with semantic search and LLM grounding
- User authentication and multi-organization data isolation
- Production-grade error handling, logging, and rate limiting
- React frontend with real-time UI updates
- Docker containerization for easy deployment
**Out of Scope (for now):**
- Mobile apps (but the web app is responsive)
- Real-time collaboration (single-user per document)
- Advanced CMS or document versioning
- Integration with legal case management systems (future roadmap)
- Audit trails or compliance logging (beyond basic auth logs)
### Main Features
| Feature | What It Does | Why It Matters |
|---|---|---|
| **RAG Pipeline** | Upload PDF → parse text → split into chunks → embed with Gemini/OpenAI → store in pgvector → retrieve top-K matching chunks based on semantic similarity → feed to LLM for grounded answer | Answers are cited — the LLM tells you exactly which clause it's basing the answer on |
| **Clause Classification** | Each chunk gets classified as termination, liability, payment, etc. | Users can filter or focus on specific clause types |
| **Risk Scoring** | Each clause gets a risk level (high/medium/low) based on deviation from standard language | Paralegals and lawyers review the high-risk items first instead of reading 50 pages |
| **Chat Interface** | Ask natural language questions; get answers with source excerpts | Non-technical users can find contract info without manual search |
| **Multi-Org Auth** | Sign up creates an org + user; every document is org-scoped | Each customer's contracts stay private; org A cannot read org B's data |
| **Async Analysis** | Upload doesn't block on AI processing; user can ask questions while analysis is running | Better UX than "please wait 60 seconds" |
| **Error Recovery** | Rate limiting, validation, timeout handling, graceful LLM failures | Won't crash on edge cases; won't run up huge bills |
### Modules
```
Backend (FastAPI + PostgreSQL + pgvector)
├── Auth (JWT + password hashing)
├── RAG Pipeline (parse → chunk → embed → retrieve → answer)
├── Risk Analysis (classify + score clauses)
├── Logging & Error Handling
├── Rate Limiting
└── API Endpoints (24 routes)
Frontend (React 18 + Tailwind-equivalent CSS)
├── Auth Pages (signup/login)
├── Document Upload
├── Risk Dashboard
├── Clause Browser
├── Chat Q&A
└── Sidebar Navigation
```
### User Roles
| Role | Who | Permissions |
|---|---|---|
| **Org Admin** | First user created at signup | Full access to all org's documents, but cannot see other orgs' data |
| **Org Member** | Additional users added later (infrastructure exists, but no UI yet) | Same as admin for now |
| **Anonymous** | No login | Can sign up or log in |
### Functionalities
1. **Authentication**: Signup creates org + user; login returns JWT token
2. **Document Management**: Upload, list, retrieve, delete documents
3. **Processing**: Auto-extract text, chunk, embed, store embeddings
4. **Analysis**: Scan all chunks for clause type + risk level + explanation
5. **Retrieval**: Semantic search across document corpus
6. **Q&A**: Answer questions with AI, cite sources
7. **Logging**: Every request logged with ID, method, path, status, duration
8. **Rate Limiting**: Per-org or per-IP throttling on expensive endpoints
9. **Error Handling**: Clean, non-leaking error messages
### Business Goals
1. **Demonstrate RAG competency**: Show that you understand embeddings, similarity search, and grounded generation
2. **Prove production readiness**: Logging, error handling, auth, and deployment work out of the box
3. **Enable fast sales cycles**: "Here's a working demo; here's the code on GitHub" — no more explanation needed
4. **Support billable services**: Model to charge per-contract-analyzed or per-month subscription on Upwork
### Project Vision
From a prototype that works locally to a deployed, revenue-generating service: ClauseIQ proves that modern AI engineering isn't just about clever prompts — it's about systems thinking, reliability, and user experience.
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