Useful knowledge is buried in PDFs, slide decks, shared drives, tickets, research, and old project folders. Search depends on knowing what already exists.
05 / RAG Systems
RAG Knowledge Assistants
Give teams answers grounded in approved company knowledge, with sources they can inspect.
Build private RAG systems that search approved company knowledge and return grounded answers with citations and access controls.
We treat retrieval quality, permissions, and evaluation as the product foundation. The assistant answers only from approved sources, cites evidence, and records what users accessed and where answers failed.
What the build includes
A complete production workflow.
Document ingestion
PDF, PowerPoint, Word, spreadsheet, transcript, and supported repository ingestion with metadata and deletion handling.
Hybrid retrieval
Vector search, keyword search, reranking, filters, chunking, and source-aware context assembly.
Secure assistant
Chat, search, or API access with SSO, RBAC, document permissions, citations, and audit logging.
RAG evaluations
Representative questions, retrieval metrics, answer checks, regression tests, and feedback-driven improvement.
Technology and fit
Built with the right tools, not every tool.
The exact stack follows the workflow, security requirements, existing systems, and deployment environment.
Technologies
pgvectorQdrantPineconeOpenSearchClaudeOpenAIAzure OpenAILlamaIndexLangChain
Industries
Professional servicesResearchLegalHealthcareSupportRegulated teams
Expected outcomes
- →Faster internal research
- →Cited answers
- →Permission-aware retrieval
- →Reusable company knowledge
- →Measurable search quality
Common questions
Before we scope it.
What is RAG?
Retrieval-augmented generation searches approved sources before the model answers. It gives the model relevant evidence and allows the response to cite where the information came from.
Can a RAG system run inside our own cloud?
Yes. We can deploy ingestion, storage, vector search, APIs, logs, and the interface inside the client environment and use only approved model providers.
How do you know retrieval quality is improving?
We build an evaluation set, track retrieval and answer behavior, and run regression tests whenever chunking, embeddings, reranking, prompts, or models change.
Next step
Discuss this system ↗Start with one useful release.
Show us the current process. We will recommend the first build, integrations, price range, and what should wait.