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Allivista for Enterprise

Document intelligence that cites the exact passage.

For teams where a wrong citation is a liability — legal, finance, compliance, insurance, medical. Allivista retrieves the verbatim passage and shows its source, so an answer can be verified, not just trusted. It's structural, not generative: exact by construction, with no hallucinated citation surface — and it runs on your own infrastructure.

Built for a security review
Six properties a frontier AI vendor structurally can't match — because model-agnostic and zero-knowledge are the opposite of how they make money.

Exact by construction

Recall returns the verbatim span from your documents and cites the source. There is no generative step between the corpus and the citation, so a made-up reference isn't unlikely — it's impossible.

no hallucination surface

Zero-knowledge & on-prem

The index runs inside your environment and stays there — air-gappable. Authentication needs no cloud credentials on the box. We can operate as a processor that never sees your documents.

air-gappable

Physical tenant isolation

Each organization is a separate index on disk — separate segments, separate logs. Cross-tenant leakage isn't blocked by a filter you could forget; it's impossible by construction.

separate stores, not shared rows

Provable deletion

Delete retires a document immediately — it vanishes from recall the moment it's tombstoned, and you can show it's gone. Updates supersede in place; no segment is ever silently rewritten.

the compliance ask, answered

Model-agnostic

The structural layer stands alone. Pair it with Claude, an open model, or a local LLM for phrasing — swap models without re-indexing. You're not married to one vendor's roadmap or pricing.

bring your own model

Live, auditable index

Add, update, and delete documents while the index serves queries — reads see the change immediately. Every write is append-only and durable, so the corpus is always current and always accountable.

changes searchable instantly
Why not just an LLM on your documents?
RAG is approximate retrieval feeding a model that guesses. Allivista is exact retrieval that cites.
LLM-on-RAG
  • Retrieves the closest chunk by embedding similarity
  • The model paraphrases — citations can drift or be invented
  • Accuracy degrades as document counts and chains grow
  • Tenancy and deletion are policy layers you have to trust
Allivista (structural)
  • Returns the exact matching span, byte-for-byte
  • The source is the answer — the model only reads it aloud
  • Exact stays exact at any scale; reliability doesn't decay
  • Isolation and provable deletion are architectural, not policy
Who it's for
Legal & e-discovery Finance & audit Compliance & risk Insurance Medical & clinical Technical & engineering docs
0hallucinated citations, by construction
<1 msstructural lookup to the exact passage
100%of writes append-only & auditable

Start a pilot on your own documents.

We'll stand up an isolated index on your infrastructure and index a slice of your corpus — you'll see the citations for yourself.

Zero-knowledge · on-prem or private cloud · DPA & processor terms available