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I will engineer enterprise ai copilots and graph rag systems
Faizan
About this gig
Out-of-the-box AI chatbots hallucinate and leak sensitive data. I build secure, zero-hallucination Enterprise RAG systems and private AI Copilots over your internal data assets.
Whether you need to search thousands of internal PDFs, sync live databases, or build a company-wide assistant, I engineer high-precision vector search engines that cite exact sources with 100% data privacy.
What I Build For Your Organization: Hybrid Vector Search: Dense Vector (Pinecone/Qdrant) + BM25 retrieval for high accuracy. Multi-Source Ingestion: Sync with Google Drive, Notion, Postgres, and local documents. Citation Guardrails: Verification loops ensuring answers cite the exact document line. Token & Cost Controls: Semantic caching layers to reduce monthly LLM API bills. Enterprise Security: Role-based access control preventing data leaks.
My 3-Step Execution Blueprint:
- Document Embedding: Optimizing text splitters for maximum semantic recall.
- Vector Pipeline Construction: Fail-safe retrieval pipelines in Python or n8n.
- Production Deployment: On-premise or cloud deployment with a 30-Day Guarantee.
Click "Contact Me" to build your private copilot today!
Please Discuss Before Placing Order!
Get to know Faizan
Faizan
Enterprise AI Architect, Autonomous Swarms, MCP, GraphRAG, Claude, n8n
- FromPakistan
- Member sinceFeb 2024
- Avg. response time1 hour
- Last delivery1 week
Languages
English
As an AI & Automation Architect with +5 years of experience,
I engineer self-healing, zero-downtime AI ecosystems built for horizontal scale & strict data sovereignty (SOC2/GDPR mindset).
I bridge the gap between experimental AI and production ready enterprise software
★ CORE STACK:
• Agent Swarms (LangGraph, CrewAI)
• Enterprise GraphRAG (Neo4j, Pinecone)
• Custom MCP Servers (Claude, APIs)
• Production Backends (Python, FastAPI, Docker, n8n)
Message me your bottleneck to map out your roadmap.
My Portfolio
Other Software Development Services I Offer
FAQ
Q: How do you guarantee the AI won't hallucinate or invent false information?
A: I implement strict system guardrails and hybrid retrieval (BM25 + Vector). The copilot is explicitly instructed to answer strictly using retrieved context and cite source document links. If context is missing, it responds "Data not found" rather than guessing.
Q: Is my internal company data safe and private?
A: Yes. Your data is indexed into private, encrypted vector instances (Pinecone, Qdrant, or local PGVector). Data is never used to train public LLM models, and on-premise execution pathways are available for strict compliance needs.
Q: What document formats can the RAG engine process?
A: The system ingests PDFs, DOCX files, CSVs, Notion pages, Google Drive files, website URLs, and SQL/Postgres databases with automated document chunking and vector re-indexing.
