RAG Document Intelligence — AI-Powered Document Search System by Ahmad FarazRAG Document Intelligence — AI-Powered Document Search System by Ahmad Faraz
RAG Document Intelligence — AI-Powered Document Search SystemAhmad Faraz
Cover image for RAG Document Intelligence —  AI-Powered Document Search System
Your team is searching manually through documents that an AI could answer in seconds. I build RAG (Retrieval-Augmented Generation) systems that let your team ask questions in plain English and get accurate answers from your own documents instantly — contracts, policies, reports, manuals, anything. What I build: → Document ingestion pipeline (PDF, Word, Excel, web pages) → Intelligent chunking and vectorization into Pinecone → Natural language query interface (Slack, web app, or chat widget) → Source citation with every answer so staff can verify accuracy → Access control so different teams see only their documents → Supabase database for document management and logging Real result: Retrieval accuracy improved from 60% to 90%+ after rebuilding a client's pipeline with proper semantic chunking and metadata enrichment. Information retrieval time went from hours to seconds. This system is ideal for: ✓ Legal and compliance teams ✓ Real estate agencies with large contract libraries ✓ HR teams managing policies ✓ Finance teams with large report archives ✓ Any team that spends time searching for information manually Includes full technical documentation and staff onboarding guide.
FAQs

Example work
Title: Autonomous B2B Client Portal &
Starting at$3,500
Duration3 weeks
Tags
Document Processing
OpenAI
Pinecone
Python
RAG
Slack
Supabase
LLM
Artificial Intelligence
Service provided by
Ahmad Faraz Bahawalpur, Pakistan
5.00
Rating
1
Followers
RAG Document Intelligence — AI-Powered Document Search SystemAhmad Faraz
Starting at$3,500
Duration3 weeks
Tags
Document Processing
OpenAI
Pinecone
Python
RAG
Slack
Supabase
LLM
Artificial Intelligence
Cover image for RAG Document Intelligence —  AI-Powered Document Search System
Your team is searching manually through documents that an AI could answer in seconds. I build RAG (Retrieval-Augmented Generation) systems that let your team ask questions in plain English and get accurate answers from your own documents instantly — contracts, policies, reports, manuals, anything. What I build: → Document ingestion pipeline (PDF, Word, Excel, web pages) → Intelligent chunking and vectorization into Pinecone → Natural language query interface (Slack, web app, or chat widget) → Source citation with every answer so staff can verify accuracy → Access control so different teams see only their documents → Supabase database for document management and logging Real result: Retrieval accuracy improved from 60% to 90%+ after rebuilding a client's pipeline with proper semantic chunking and metadata enrichment. Information retrieval time went from hours to seconds. This system is ideal for: ✓ Legal and compliance teams ✓ Real estate agencies with large contract libraries ✓ HR teams managing policies ✓ Finance teams with large report archives ✓ Any team that spends time searching for information manually Includes full technical documentation and staff onboarding guide.
FAQs

Example work
Title: Autonomous B2B Client Portal &
$3,500