Retrieval-augmented generation lets an AI system answer from your actual documents, policies, and data — instead of guessing from what a model happened to be trained on. Done right, it's the difference between a demo and a tool people trust.
We handle the parts that actually determine RAG quality: chunking strategy, embedding model choice, hybrid search (semantic + keyword), re-ranking, and citation so every answer traces back to a source.
The result is a retrieval layer you can drop behind a chatbot, a search bar, or an internal knowledge tool — tuned against your real documents, not a generic benchmark.
What's included
Document ingestion & chunking pipeline
Vector database setup (Pinecone, pgvector, etc.)
Hybrid semantic + keyword search
Re-ranking for answer precision
Source citation on every response
Continuous re-indexing as documents change
Ideal for
- Internal knowledge base search
- Customer support grounded in docs
- Legal, medical, or compliance-heavy content
You'll walk away with
- Retrieval pipeline & vector store
- Evaluation report on retrieval accuracy
- API for integration into your app
- Re-indexing automation