APFlow / Integration engineering
RAG development
Get useful answers from your own documents, with a route back to the source. We build retrieval augmented generation systems and evaluate them against questions your team actually asks.
Discuss your project15 minutes. Start with one workflow.
Is this the right fit?
For teams searching policies, product documentation, internal knowledge, or support content across disconnected sources. RAG fits questions grounded in documents; it does not replace a transactional database query or a tool that performs an action.
Example workflow
Before
An assistant gives a plausible answer from an outdated document. The reader cannot tell which source it used or whether that source is current.
After
The system retrieves relevant passages from approved sources, preserves document access rules, and returns an answer with citations. Low-evidence questions are flagged instead of presented as settled facts.
What you get
A maintained knowledge pipeline
Document parsing, chunking, metadata, indexing, and an update strategy. Source identity and access boundaries remain part of the data rather than being added after retrieval.
Retrieval tuned to your questions
Search, filtering, reranking where useful, and cited responses. We assess where simple search is enough and where a generated answer adds value.
Evaluation and handover
A representative question set, reviewable results, and checks for unsupported answers. Source code, configuration, and runbooks let your team keep evaluating quality as the documents change.
Before you book
Does RAG eliminate hallucinations?
No. Retrieval supplies evidence, but the model can still misread or misuse it. We use source citations, grounded-answer evaluation, and an explicit path for unanswered questions to make failures visible.
Can users only see answers from documents they can access?
That is a design requirement to establish during scoping. Document permissions need to be carried into indexing and enforced during retrieval. We test the access boundaries using representative user roles.
Should we choose RAG or MCP?
Choose retrieval for finding and explaining document content. Choose MCP tools when an agent needs to read live application state or perform scoped actions. Some products need both, with clear boundaries between answering and acting.