Search and Q&A over your internal documents and databases with controlled retrieval, source citations, access rules and measured accuracy.
Retrieval-augmented Q&A over your internal knowledge — grounded, permissioned and continuously evaluated.
Internal knowledge bases
Customer support deflection
Policy and compliance search
Technical documentation Q&A
Research and analyst workflows
Retrieval architecture
Ingestion and indexing pipeline
Permission and citation layer
Evaluation harness with ground-truth set
Monitoring and refresh strategy
Answers backed by retrieved passages with source citations — never generic model guesses.
Users only see what they're allowed to; access rules enforced at the retrieval layer.
New and updated content is picked up automatically so answers stay current.
Ground-truth evaluation set and monitoring so answer quality is measured, not assumed.
Map the sources, the actual questions users ask, and the accuracy bar the answers must meet.
Design ingestion, chunking, retrieval, permission model and evaluation methodology.
Build on a representative slice of your data, measure accuracy on real queries.
Deploy, monitor answer quality and drift, extend to more sources.
An AI knowledge base over 10,000+ pages of technical documentation — equipment manuals, maintenance procedures, safety standards — with hybrid search and grounded answers for plant operators and engineers.
View case study →A GraphRAG backend for legal document analysis — clause extraction, cross-document reasoning, and grounded answers over contracts, statutes and case law — used by 1,000+ lawyers in production.
View case study →An AI agent that parses Visa and Mastercard rulebooks, predicts interchange fees per transaction and answers internal compliance questions grounded in card-network documentation.
View case study →Traditional search returns links. RAG returns grounded answers with citations, in the format your users actually need.
Document stores (Confluence, SharePoint, Notion, Google Drive, S3), wikis, ticketing systems, databases and internal APIs.
Answers must be grounded in retrieved passages; anything not supported is flagged. Accuracy is measured on a ground-truth set, not just checked ad-hoc.
Yes. Retrieval enforces the same access rules as your source systems — users only see content they already have permission to read.
Book a technical call. We'll review your workflow, data, integrations and constraints, then recommend what is worth prototyping.
