I will build a custom rag pipeline with your data using llms


About this gig
Turn your documents into a smart AI chatbot with a custom RAG (Retrieval-Augmented Generation) system.
Generic AI doesn't know your specific business data. I build production-ready RAG pipelines connecting your private knowledge base to LLMs so your assistant answers accurately, cites real sources, and never hallucinates.
What I Design & Build:
- Document Ingestion: PDFs, CSVs, websites, Notion, Google Drive
- Vector Database Setup: Pinecone, Qdrant, ChromaDB, Weaviate, Supabase pgvector
- Smart Retrieval: Embeddings, hybrid search, and Cohere reranking for maximum accuracy
- LLM Integration: OpenAI GPT, Anthropic Claude, Google Gemini, or self-hosted models (Ollama)
- n8n Automation: Auto-syncing workflows to keep your knowledge base constantly updated
- Prompt Engineering: Guardrails for grounded, citation-backed answers
- Deployment: API endpoints, webhooks, or messaging integrations (Slack, WhatsApp)
Perfect For:
- Customer support chatbots
- Internal company knowledge bases
- Document Q&A & research tools
I build and manage self-hosted AI infrastructure and n8n workflows daily this isn't theory, it's my exact toolkit.
Get to know Mubashir S
AI Engineer Automation Specialist n8n Python Expert
- FromPakistan
- Member sinceMar 2026
Languages
Urdu, English
FAQ
Can you handle large datasets (10,000+ documents)?
Yes — this is scoped in the Premium package with chunking strategy and vector DB indexing built for scale.
Can this integrate with n8n / Slack / WhatsApp?
Yes — I have deep hands-on experience with n8n workflow automation and can wire the RAG pipeline into almost any messaging or business tool.
What's the difference between RAG and just fine-tuning an LLM?
RAG retrieves relevant info from your data at query time and feeds it to the LLM as context — no retraining needed, always up to date, and answers can cite sources. Fine-tuning bakes knowledge into the model itself and is harder to update.

