I will build a production ready rag pipeline for your documents and knowledge base

Your documents contain valuable knowledge. Your team can't find it fast enough.

Most RAG implementations fail in production because they're built for demos.

I take a diagnostic approach: chunking strategy, embedding model, and 

retrieval tuned specifically for your documents and use case.

WHAT YOU GET

Fully functional RAG pipeline (Python-based)

Vector store setup (Chroma, Pinecone, or Weaviate)

Chunking and retrieval tuned for your document structure

Clean, documented code you can maintain

Technical handoff document

7 days post-delivery support for bugs and integration questions

GOOD FIT

PDFs, docs, or text files you want to query in natural language

Internal assistant or customer-facing chatbot

Existing RAG prototype that isn't performing well

NOT A GOOD FIT

Real-time / live web scraping pipelines

Full-stack app with frontend UI

Ready-to-use web application or hosted chatbot interface 

(this gig delivers backend code, not a deployed product)

Before starting, I ask a few questions to understand your documents, use case, 

and performance requirements. The right solution depends on the diagnosis.

Message me before ordering if you're unsure which package fits.