I will build a rag pipeline with pinecone, supabase or a vector database

Turn your documents, knowledge base, or business data into a reliable AI retrieval system that answers with relevant context.

I will design and build a custom retrieval-augmented generation (RAG) pipeline using Pinecone, Supabase pgvector, Qdrant, Weaviate, Chroma, or another suitable vector database.

WHAT YOU RECEIVE

Data ingestion and cleaning

Document chunking and metadata design

Embeddings and vector indexing

Semantic or hybrid retrieval

GPT, Claude, or compatible LLM integration

Source citations and prompt controls

API endpoint or app integration

Testing, evaluation, and handover notes

USE CASES

Private knowledge assistants

Customer support search

Internal document Q&A

Research and compliance tools

Product or technical documentation

I can work with PDFs, websites, Notion exports, CSV files, databases, or APIs. Package scope determines source count, deployment, and evaluation depth.

Please message me before ordering if your data is sensitive, multilingual, very large, or requires private-cloud deployment.

Important: AI outputs can be imperfect. I build safeguards and evaluation checks, but no system can guarantee error-free answers.