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English
$
USD
I will build custom enterprise rag pipelines and private ai
Abdullah MJamal
About this gig
Are generic AI bots hallucinating on your critical business data?
I architect, deploy, and harden production-grade Retrieval-Augmented Generation (RAG) pipelines and private document AI systems that ground frontier LLMs in your proprietary knowledge base.
Whether using cloud vector stores or 100% private, on-premise Docker deployments running local open models, your sensitive data remains accurate, verifiable, and secure behind your firewall.
Architectural Capabilities:
- Document Ingestion: High-precision parsing of complex PDFs, financial sheets, tables, and internal wikis with semantic chunking.
- Hybrid Search & Reranking: Dense vector search with BM25 keyword matching and Cohere rerankers for sub-second, pinpoint context fetching.
- Vector DB Setup: Production indexing on Pinecone, Qdrant, Milvus, ChromaDB, or pgvector.
- Private Deployments: Containerized setups via Docker with local models for zero data leakage.
- Custom Endpoints & UI: Clean REST API endpoints or lightweight interfaces (Streamlit) for your team.
All pipelines feature strict prompt guardrails and source citations.
Please message me before ordering to align on your data schemas and security constraints.
Get to know Abdullah MJamal
Abdullah MJamal
AI Solutions Architect, RAG and Automation Specialist
- FromPakistan
- Member sinceAug 2022
- Avg. response time1 hour
Languages
Urdu, English
I build production-grade Agentic AI workflows and high-precision RAG pipelines that turn your data into autonomous business assets. Backed by a background in Computer Network & Security, I ensure every LLM orchestration is secure and scalable.
My Expertise:
• Advanced RAG Architecture (Claude, Gemma, NeMo)
• Custom Agent Frameworks & OpenClaw
• Complex n8n Workflow Automation
• Secure API Integrations
Let’s transform your disjointed tools into an intelligent AI ecosystem.
FAQ
How do we prevent the AI from hallucinating?
We implement strict system guardrails, hybrid search (semantic vector + BM25 keyword matching), and reranking models. The LLM is strictly constrained to synthesize answers only from the retrieved chunks, returning verified source citations.
Can this system run 100% privately without sending data to third parties?
Yes. I can deploy an air-gapped, containerized pipeline via Docker on your private VPS or local hardware using high-performance open-weight models, ensuring zero proprietary data leaves your environment.
Which document formats can the pipeline ingest?
The pipeline handles standard and complex layouts including unstructured PDFs, scanned documents via OCR, Word documents (.docx), spreadsheets (.csv, .xlsx), markdown, and database dumps.
