Enterprise AI that actually knows your documents.
medhAṪ pairs a fine-tuned LLaMA 3.1 model with a private vector knowledge base, so your teams can ask questions of your own documents and get accurate, sourced answers — without your data ever leaving your infrastructure.
medhAṪ is built around two complementary engines that share the same underlying model and knowledge base.
Upload a document and ask questions in plain language. medhAṪ reads it, finds the answer, and can generate a downloadable executive summary on request.
Feed in an RFP and get a structured draft proposal back, built on the same model and knowledge base used for document Q&A.
Drop in a PDF, PPT, or DOC file — or connect a knowledge base.
Type a question the way you'd ask a colleague. No special syntax.
medhAṪ searches the vector knowledge base and reasons over the result.
Get an answer in chat, or download an executive summary.
Any team that spends hours searching files, answering repetitive questions, or drafting long-form responses is a fit.
Give employees one place to ask questions across policies, manuals, and internal knowledge bases.
Turn incoming RFPs into structured first-draft proposals, then refine and export for submission.
Get consistent, sourced answers from policy documents and regulatory filings on demand.
Resolve repetitive questions instantly by pointing medhAṪ at manuals, FAQs, and runbooks.
Help recruiters and delivery teams answer client and candidate questions straight from project docs.
Stress-tested against an 800-page technical document and across five subject areas at once.
medhAṪ runs wherever your data already lives — not the other way around.