We turn scattered documents, wikis, contracts, and tickets into a single answer engine. Every response cites its sources, and every query respects who is allowed to see what.
Ingestion, ranking, citations, and access control — the full layer that makes retrieval trustworthy at work.
Semantic plus keyword retrieval, tuned on your actual content and real queries — not a generic benchmark.
Every response links to the source passages it was built from, down to the page.
Users only ever get answers from documents they're permitted to see, checked per query.
A graded test set from real questions, so accuracy is measured and improves over time.
See what people ask, where answers fail, and which knowledge gaps to fill.
The retrieval topology and the query pattern — not a simplified marketing diagram.
// elhaa Hybrid Retrieval Query const results = await elhaaRetrieve.query({ text: userQuery, mode: 'hybrid', // bm25 + vector rerank: true, topK: 8, citeSourcesOnly: true, aclContext: user.permissions });
Hundreds of internal policy questions a month, answered from memory by two senior staff who'd become an unofficial bottleneck for the entire compliance team.
We ingested the policy library and past clarifications into a permission-aware RAG system with citations on every answer, and a hard rule: if sources don't support an answer, say so and route to a human.
*Illustrative example based on a representative engagement.
Map the document sources, formats, permissions, and the questions people actually ask.
A working retrieval system on a representative slice of your content.
Iterate on chunking, ranking, and prompting against a real question set.
Production rollout with access control, monitoring, and index freshness jobs.
Does the system find the right passages for real questions?
Share of answer claims actually supported by the cited sources.
How often “I don't know” is said exactly when it should be.
Thumbs-up rate and repeat usage from real users.
Yes — OCR and layout-aware parsing are part of the ingestion pipeline. Quality varies by scan, so the corpus audit flags problem documents early.
RAG dramatically reduces fabrication because answers must be grounded in retrieved passages, and we configure the system to decline when sources are insufficient. Our benchmarks measure exactly this.
Wherever your security policy requires — your cloud tenant, your VPC, or on-premises. Document content stays inside your boundary.
Configurable per source — from near-real-time for critical repositories to daily sync for archives. The admin console shows index freshness so you always know what the system has seen.
Yes. Modern embedding and generation models handle major languages well, including mixed-language corpora — a document ingested in one language can be asked about in another. We include your actual language mix in the benchmark set to verify quality rather than assume it.
A 30-minute call. We'll tell you honestly whether this is the right solution — and what it would take.
A short form, then a 30-minute call. We reply within one working day.
We'll be in touch within one working day.