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I will develop a production rag ai assistant and semantic search engine
Ahmad
About this gig
Are generic AI chatbots answering your business questions with hallucinations and vague guesses? You need a dedicated Retrieval-Augmented Generation (RAG) assistant grounded strictly in your proprietary documentation.
What I Deliver:
- High-precision document ingestion & chunking (PDFs, Markdown, CSV, Docs)
- Vector store setup (ChromaDB / Pinecone / FAISS / Qdrant / Local In-Memory)
- Semantic vector retrieval paired with exact keyword filtering
- Strict context grounding with exact page/section source citations
- FastAPI REST endpoints and clean web UI for user queries
- Automated evaluation tests to prevent hallucination
Bring your technical documents, policy manuals, or financial records to life with verifiable AI intelligence.
Get to know Ahmad
Ahmad
AI Systems and Full Stack Automation Engineer
- FromPakistan
- Member sinceOct 2024
- Avg. response time1 hour
Languages
Urdu, Punjabi, English
Hi, I'm Ahmad Hassan, an AI Systems & Full-Stack Automation Engineer. I engineer production Python automation pipelines, Model Context Protocol (MCP) servers, high-converting SaaS web apps, and enterprise RAG search engines.
My Core Expertise:
- Custom MCP Servers & Agentic Workflows (Claude, Cursor, Antigravity)
- Production RAG & Semantic Search (LangChain, ChromaDB, FAISS, Citations)
- Modern Full-Stack SaaS Apps (FastAPI, React, Tailwind CSS, SQL)
I focus on verifiable engineering, clean architecture, and reliable AI systems.
My Portfolio
FAQ
How does the assistant prevent hallucination?
The LLM prompt is strictly constrained to synthesize answers only from retrieved chunks and must cite the exact source reference for every claim.
Can it run with local vector storage without expensive cloud databases?
Yes! I can configure lightweight local vector indices (ChromaDB, FAISS, or SQLite vector) that run entirely on your own infrastructure.
What document types are supported?
PDFs, Word documents, text/markdown files, spreadsheets, and structured JSON.
