# 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.

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## 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**.

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## 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**.

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## Core Principles
- No guessing
- No hidden sources
- No blind automation
- Refusal is a feature, not a failure

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## 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)

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## 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**.

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## 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

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## Project Structure
project-root/
├── frontend/
│ ├── pages/
│ ├── components/
│ └── services/
├── backend/
│ ├── main.py
│ ├── rag/
│ ├── verification/
│ ├── automation/
│ └── requirements.txt
├── docs/
└── README.md

yaml
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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**.

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## Local Setup (Backend)
```bash
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.