RAG development services that ground AI answers in your own documents

When someone asks a question, the system first searches your indexed content for the passages most likely to contain the answer. Those passages go to the language model together with the question, and the model writes an answer using only that material, with a citation for each claim.

The same pipeline can power a RAG chatbot on your website, an internal assistant for staff, or an agent that acts on what it finds. If the search finds nothing relevant, a well-built RAG system says so. 'I can't find this in your data' is a correct answer, and we design and test for it.

Mavick, our AI operating system for textile factories, gives every department its own agent that answers from that department's documents and live ERP data. Each citation shows the source and when the figure was fetched from the ERP. A member who opens another department's workspace gets a page-not-found response, and the attempt is written to the activity trail.

We bring the same pattern to client builds. Retrieval is scoped by permission, every answer carries its sources, and the system says plainly when the data does not contain the answer.

We build a test set from questions your team actually asks, each paired with the document or record that holds the answer. Two scores matter. The first is whether search returned the right source. The second is whether the answer stayed faithful to that source and added nothing it does not say.

The set runs before launch and again whenever the chunking, the embedding model or the language model changes. A change that looks harmless, such as a new model version, cannot lower answer quality without the scores showing it.

• Questions taken from real tickets, emails and staff requests
• Expected source documents for each question
• Questions the data cannot answer, to check the system refuses them
• Questions asked by different roles, to check permissions hold

Cost depends on how many sources you connect, how messy the content is (scanned PDFs and spreadsheets take more work than clean text), how strict the access rules are, and where the system must run. A single document library behind a chat interface is a much smaller project than a permission-aware index across five systems.

We scope this during discovery and give a fixed price for the build. Running cost depends on volume and the model you choose, and we keep it down with caching and smaller models for simple steps.

• Number and type of data sources
• State of the content: clean text, scans, spreadsheets
• Access rules and who may see what
• Where it runs: our cloud, yours, or on premises