Map the knowledge
Audit sources, ownership, freshness, permissions, document types, and answer expectations.
Code Huddle Solutions
RAG Application Development
We build RAG systems that connect language models to trusted business information while making retrieval quality, citations, and access control visible.
Discuss your projectWho this is for
- Organizations with private documents, support knowledge, policies, product data, or operational information that users need to search conversationally.
Problems we solve
- Answers that are not grounded in current business data
- Documents with inconsistent formats and metadata
- Missing citations and difficult-to-debug retrieval failures
- Sensitive content leaking across users or tenants
How we work
A practical path from idea to reliable delivery
Build ingestion
Normalize, chunk, enrich, embed, index, and version content with repeatable jobs.
Improve retrieval
Tune hybrid search, filters, reranking, context windows, and citation formatting.
Evaluate continuously
Maintain representative question sets and track groundedness, relevance, latency, and cost.
Scope and investment
Start with the smallest valuable scope
A pilot can usually be scoped around one knowledge domain and one user role. Production scope grows with connectors, tenant isolation, document volume, freshness requirements, and evaluation depth.
Technology patterns
- pgvector
- Pinecone
- Weaviate
- LlamaIndex
- LangChain
- Python
- FastAPI
- PostgreSQL
- Redis
Evidence and related work
- AI-focused case studies
- Generative AI service
Tradeoffs we make explicit
- Vector search alone is not enough for exact identifiers or structured filters; hybrid retrieval is often better.
- More context can improve recall but increase latency and model cost.
- Permission-aware retrieval adds complexity but is essential for private data.
Questions
Frequently asked
Can a RAG system use our private documents?
Yes. The architecture can keep documents, embeddings, permissions, and audit data inside your approved infrastructure while using a model provider that meets your policy.