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Trust-First AI Copilot
Perplexity-Style • System-Driven • No Custom LLM Deployed Link: https://trust-first-ai.vercel.app/
A trust-first AI Copilot that delivers verified, source-grounded, and confidence-scored answers using strict system rules — inspired by Perplexity and designed to fix the core limitations of modern AI copilots.
Problem
Most AI copilots today:
- Produce confident but incorrect (hallucinated) answers
- Lose context in long or multi-file documents
- Hide sources and assumptions
- Provide limited admin visibility and control
- Encourage blind dependency on AI outputs
These issues lead to wrong decisions, rework, and low trust.
Solution
This project implements a system-first AI Copilot where:
- Retrieval is mandatory (no context → no answer)
- Every answer is backed by sources
- Confidence is explicitly shown
- Low-confidence answers are refused
- Automation is human-approved
- Security, transparency, and auditability are built-in
The focus is system design over model size.
Core Principles
- No guessing
- No hidden sources
- No blind automation
- Refusal is a feature, not a failure
Key Features
- Mandatory Retrieval-Augmented Generation (RAG)
- Source-linked answers with citations
- Confidence scoring (High / Medium / Low)
- Automatic refusal on insufficient data
- Workspace / project-level context memory
- Intent detection and auto-clarification
- Human-in-the-loop automation (n8n)
- Zero-trust data access
- Full audit logs (OpenTelemetry)
- Model-agnostic LLM layer (Groq)
System Architecture
User → Intent Detection → Search & Retrieval (Tavily + Vector DB) → Context Ranking & Filtering → LLM (Groq – language & reasoning only) → Verification & Confidence Engine → Answer + Sources + Assumptions → (Optional) Human-Approved Automation → Audit Logs & Admin Dashboard
What This Project Is Not
- Not a chatbot
- Not prompt-dependent
- Not blind AI
- Not a Copilot replacement
This is a controlled, transparent, enterprise-ready AI system.
Tech Stack
Frontend
- Next.js
- React
- Tailwind CSS
Backend
- FastAPI (Python)
AI & Data
- LLM: Groq (LLaMA / Mixtral)
- Search: Tavily API
- Embeddings: Hugging Face / Local models
- Vector DB: FAISS / Qdrant
- Automation: n8n
- Logging & Audit: OpenTelemetry
Deployment
- Frontend: Vercel
- Backend: Render
Project Structure
project-root/ ├── frontend/ │ ├── pages/ │ ├── components/ │ └── services/ ├── backend/ │ ├── main.py │ ├── rag/ │ ├── verification/ │ ├── automation/ │ └── requirements.txt ├── docs/ └── README.md
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Required API Keys
| Service | Purpose |
|---|---|
| Groq | LLM inference |
| Tavily | Web search |
| Hugging Face | Embeddings |
| n8n | Automation |
All API keys are stored only in backend environment variables.
Local Setup (Backend)
git clone https://github.com/your-username/your-repo.git
cd backend
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
Create .env:
env
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GROQ_API_KEY=xxxx
TAVILY_API_KEY=tvly_xxxx
HF_API_KEY=hf_xxxx
N8N_API_KEY=xxxx
N8N_BASE_URL=http://localhost:5678
Run backend:
bash
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uvicorn main:app --reload
Local Setup (Frontend)
bash
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cd frontend
npm install
npm run dev
Deployment
Backend
Push code to GitHub
Connect repository to Render
Build command:
bash
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pip install -r requirements.txt
Start command:
bash
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uvicorn main:app --host 0.0.0.0 --port 10000
Frontend
Deploy via Vercel
Set backend API URL in environment variables
Security & Trust Model
API keys never exposed to frontend
Per-user data isolation
Role-based access control
Full audit trail for AI actions
How Hallucinations Are Prevented
Retrieval is mandatory
Claims must map to sources
Confidence is evaluated
Low confidence triggers refusal
No source → No answer
Use Cases
Research and academic assistance
Enterprise internal knowledge copilots
Policy and compliance analysis
Long-document summarization
Decision-support systems
Future Improvements
Offline read-only mode
Multimodal reasoning (charts + text)
Advanced admin dashboards
Domain-specific copilots
Final Note
LLMs don’t fail — systems fail.
This project demonstrates how strong system design beats larger models.