Knowledge & Retrieval Systems

RAG, document and enterprise search, ingestion, grounded answers, and SQL plus vector retrieval.

Problems solved

Important knowledge is scattered across documents, databases, chats, and tools.
Teams need answers with source grounding instead of another generic chatbot.
Search quality depends on ingestion, chunking, permissions, and retrieval strategy.

Representative deliverables

Knowledge-source audit and retrieval architecture
Document ingestion, indexing, and refresh pipelines
Hybrid SQL, keyword, and vector retrieval workflows
Grounded answer interfaces with citation and freshness rules

Practical use cases

Internal knowledge assistant
Enterprise document search
Client or product support retrieval
Structured data plus document QA

Typical topics

RAGenterprise searchpgvectorgrounded answers

How this fits an engagement

This group can be scoped as a focused pilot first: define the workflow, connect the minimum useful data and tools, keep human oversight visible, and improve from observed behavior before expanding the system.

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