Vector Databases & RAG
Connect AI to your company's knowledge with retrieval-augmented generation
Overview
RAG is how you make AI actually know your business. We build retrieval-augmented generation systems that connect LLMs to your documents, databases, and knowledge bases — so AI answers with your data, not the internet's. Pinecone, Weaviate, ChromaDB, pgvector — we pick the right stack and build it right.
Capabilities
Document Ingestion Pipelines
Automated pipelines for PDFs, docs, databases, and APIs — with chunking, embedding, and re-indexing strategies.
Vector Search Optimization
Hybrid search, re-ranking, metadata filtering, and query expansion for high-precision retrieval.
Source Citations
Every answer includes the source documents so users can verify — essential for legal, medical, and financial use cases.
Incremental Updates
Knowledge bases that stay fresh as your documents change — no full re-indexing required.
Use Cases
- Internal knowledge base Q&A
- Customer support with product docs
- Legal contract and policy search
- Research library semantic search
- Sales enablement and competitive intel
- Compliance and audit documentation
Ideal For
- Companies with lots of internal documentation
- Support teams answering the same questions
- Knowledge workers searching across systems
- Any business where answers live in documents
Frequently Asked Questions
Which vector database should we use?
Depends on scale, cost, and existing infrastructure. Pinecone is managed and easy, Weaviate is flexible, pgvector is great if you already use Postgres.
How accurate is RAG?
With proper chunking, re-ranking, and evaluation, RAG systems typically hit 85–95% accuracy on well-scoped queries.
Ready to Deploy Vector Databases & RAG?
Book a free AI Deep Dive and we'll map Vector Databases & RAG to your business needs, team capabilities, and budget.
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