We engineer retrieval-augmented generation systems that turn your private documents into accurate, citable answers for teams and customers.
Enterprise RAG is an information architecture problem first. We design ingestion for PDFs, wikis, tickets, and databases; choose chunking that preserves meaning; and pair dense retrieval with keyword search when domain terms matter.
Answers include citations so reviewers can verify claims. Access follows your identity model, role, tenant, and document ACL, so one index never leaks another business unit’s files.
Evaluation is continuous: golden questions, faithfulness checks, and latency budgets. When content changes, re-indexing and cache invalidation keep answers current without surprise downtime.
We deploy on your cloud or ours, with observability on retrieval quality and cost. The assistant becomes infrastructure, not a weekend prototype.
Frequently Asked Questions
Which document types do you support?
PDFs, Word, HTML, Markdown, Confluence/Notion exports, ticket archives, and structured tables. We prioritize the sources your teams already trust. Codoop delivers this as production software you own, in 14-day sprints, with staging demos and a full source handover. Work starts from a written architecture note and finishes with repositories, runbooks, and full client ownership of the code.
Can RAG stay inside our VPC?
Yes. We routinely deploy embeddings, vector stores, and orchestration inside your cloud account with no data leaving your boundary except approved model calls. Codoop delivers this as production software you own, in 14-day sprints, with staging demos and a full source handover.
How is this different from a chatbot wrapper?
We own the retrieval layer: chunking strategy, hybrid search, ACL filters, citations, and evals. The chat UI is one surface on top of a durable knowledge system. Codoop delivers this as production software you own, in 14-day sprints, with staging demos and a full source handover.