Solutions / Smart AI Search

Answers from your knowledge, in seconds

Semantic search and retrieval-augmented generation (RAG) over your corporate knowledge. Source-cited answers, no hallucinated facts, on-premise when you need it

SemanticSource-citedOn-prem
9:41
Knowledge Searchenterprise search On-premise
Enterprise knowledge · in production
Retrieved sources
Answer
on-prem index 12,400 docs
iPhone running Zentavor Knowledge Search
90%answer accuracy
<2 stime to answer
100%source-cited
Inside a search

It doesn't guess. It retrieves, then cites

Ask in plain language. The system searches across your silos, ranks what's relevant, and writes an answer grounded in real sources, with citations you can open

Knowledge Search
12,400 docs · on-prem
Retrieved sources
Answer
Why semantic

Keyword search matches words. People ask in meaning

Same question (“how do I rotate API keys?”), two engines. Keyword grabs literal matches; semantic understands intent

Keyword search0useful hits
Office key rotation schedule0.21weak match
Keyboard shortcuts guide0.18weak match
API reference index0.16weak match
Press kit & brand keys0.11weak match
Semantic search3relevant hits
Runbook: rotating API credentials0.93relevant
Secrets management policy0.88relevant
Vault key-rotation guide0.84relevant
Incident: leaked key response0.71related
How it works

Grounded retrieval, not a guess

A hybrid pipeline turns a question into a cited answer, and refuses to answer when nothing relevant is found

query → cited answercompleted · 1.2s
understand
intent + entities
embed
query vector
hybrid retrieve
SQL + semantic
rerank
top-k by relevance
ground + cite
answer from sources
guardrail
no source → no answer
The challenge

Keyword search misses the question

Keyword search

Keyword search matches words, not intent. Zero useful hits for real questions.
Generative answers hallucinate with no citations.
Knowledge is siloed across wikis, tickets, PDFs and databases.
Sensitive documents can't go to third-party clouds.

With Zentavor

Semantic and hybrid retrieval, with top relevance scores of 0.93, 0.88 and 0.84.
100% source-cited: no source, no answer.
Hybrid SQL + semantic retrieval across every silo.
Runs fully on-prem inside your perimeter.
FAQ

Your frequently asked questions

How do you stop the model from hallucinating?
Answers are generated only from retrieved sources, every claim is cited, and the system refuses to answer when nothing relevant is found: no source, no answer.
Does it respect who is allowed to see what?
Yes. Retrieval is permission-aware: it only searches documents the asking user already has access to, so answers never leak restricted content.
What can it connect to?
Wikis, ticketing, PDFs, file stores and databases, with hybrid SQL + semantic retrieval, so structured and unstructured knowledge answer the same question.
Can it run without sending data to third parties?
Yes. The full stack (models, embeddings and index) can run inside your perimeter, so sensitive documents never leave your environment.
How fast is it?
Time to a cited answer is under 2 seconds end-to-end: query understanding, hybrid retrieval, reranking and grounded generation all run in the same pipeline.
How do you measure accuracy?
Answer accuracy is ~90% on domain eval sets, measured by source-grounded correctness, not just retrieval recall. We run an offline eval harness on your data before go-live and monitor it in production.
Let's talk

Make your knowledge answerable

Point us at your sources: we'll scope retrieval, grounding and a deployment that fits your security

Request a demo