A 5-day engagement that maps your data, surfaces high-ROI AI candidates, and recommends a pilot — fixed price.
Read the briefFrontier-class models on isolated infrastructure — your data never leaves the perimeter.
Explore the stackInternal AI search across your full corpus — structured, unstructured, multilingual.
Your answers already exist — buried across wikis, PDFs, tickets, code, and email. RAG turns that scattered corpus into a single, access-aware mind your team can ask in plain language.
We go beyond naive vector search: hybrid sparse-plus-dense retrieval, learned reranking, freshness windows, and source-aware access control, so results are relevant, current, and only ever what the user is allowed to see.
Specific, production-grade capability — not a feature checklist.
Sparse (BM25) + dense embeddings + reranking for relevance that naive vector search misses.
Documents, code, tickets, email, and audio transcripts — across languages.
Incremental indexing and freshness windows so answers reflect today, not last quarter.
Retrieval respects per-document permissions; users never see unauthorised content.
Answers cite sources and signal confidence; low-confidence queries defer.
Retrieval and answer quality measured against a labelled set, continuously.
We catalogue your sources, formats, and access rules, then design chunking and metadata.
Documents are embedded and indexed with permission metadata, incrementally kept fresh.
Hybrid retrieval plus reranking returns the right passages; the model answers from them with citations.
A labelled eval set drives retrieval and answer-quality tuning before and after launch.
Start with a fixed-price 5-day Readiness Assessment or a 6-week pilot. Senior engineers, measurable evals, and a system you own on handover.