Funding $7.5M Series A closed — Zenith AI scales next

Product

Zenith

The AI-native ontology that turns answers into decisions you can trust. Verify. Remember. Audit. People decide. Systems execute.

Memory that lasts. Rails that scale.

Open console

Why now

AI already drafts. It still stops before the decision

Summaries and copilots are everywhere. The high-value moment — confirm the OR slate, release the report, advance the candidate — still waits on humans, because the system cannot prove the answer is decision-grade.

Go/No-Go

Deterministic verdicts with itemized reasons

Standards

RDF · OWL · SHACL · PROV-O provenance

Air-gap

Judgment path completes inside your network

Human

Evolution and high-impact actions stay approved

Complete system

Two questions. Both need a yes

Automating construction without trust produces fast wrong answers. Trust without automation stays too expensive to run. Zenith is built for both.

  • Build knowledge fast

    AI drafts the ontology from documents, records, and systems in hours or days — not months of hand-drawn maps. Experts spend time verifying and approving, not redrawing.

  • Make it trustworthy

    Automated knowledge only enters decisions after deterministic checks, belief grades, and human gates. Speed without verification is how you fail fast in regulated ops.

Decisions

Metrics tell you what happened. Zenith tells you what you may do

Keep your BI. Add the missing layer: deterministic Go/No-Go, itemized why-not, and belief grades that survive an audit.

  • Not “what happened”

    Dashboards already answer that. Zenith answers a harder question: may we make this decision — and if not, which conditions failed.

  • Go / No-Go with reasons

    Every verdict lists unmet rules item by item. Same inputs always produce the same judgment — reproducible months later in an audit.

  • Belief grades, not frozen facts

    Knowledge carries state: proposed, reviewed, asserted, inferred. Unverified guesses never weigh the same as validated truth.

Trust layer

Verification. Grades. Evolution.

The three pieces that turn an AI-drafted ontology into something you can operate — and keep alive without another redraw project.

  • Deterministic verification

    Declared rules on the ontology gate every decision. Hallucinated drafts cannot become approved GO without passing the gate.

  • Trust promotion

    Facts climb a graded ladder with provenance — when verified, by whose authority, on what evidence. Bad premises trigger re-review of dependent judgments.

  • Evolution with approval

    Operating results propose rule corrections. The system never rewrites policy on its own — humans approve, then memory updates.

Discipline

Four engines. One job for Zenith

Prediction may be wrong. Verification may not. Mixing them collapses accountability. Zenith owns verify — and validates what optimizers propose against the same ontology.

  • Verify

    Zenith’s core: ontology rules, Go/No-Go, reasons, memory, and evolution.

  • Optimize

    Solvers search schedules and allocations — then Zenith validates the candidate against the same rules.

  • Predict

    Forecasts stay probabilistic by design. Prediction never silently becomes approval.

  • Actuate

    Connectors notify and write back. Execution stays separate from judgment.

How it works

Ground truth in. Safe execution out.

Guardrails constrain what can run. The ontology holds graded knowledge. The loop turns signals into approved action and back into memory.

Guardrails

  • Allowed actions
  • Ops policies
  • Roles & approvals
  • Service catalog

Ground truth the system learns

  • DB schema
  • Code repos
  • Docs & tickets
  • Alerts
  • Change history

Automata ontology

People decide. Systems execute.

Context in · signed actions out

Continuous execution

  1. 01 Sense
  2. 02 Frame
  3. 03 Preview
  4. 04 Approve
  5. 05 Act
  6. 06 Check
  7. 07 Learn

Learn feeds the ontology · Check can re-open Frame · Mistakes restore

Alerts and tickets become proposals. Every write is previewed. Humans approve. The loop compounds into ontology.

The loop

Watch. Decide. Ship. Learn.

Observability, Zenith, and Zenith Editor form a closed cycle. People stay on planning, framing, and priorities.

  • Observability watches

    New Relic, Datadog, Sentry, and webhooks feed an incident inbox. Sensitive fields are redacted before humans see them.

  • Zenith decides

    Ontology + service catalog frame the ticket. Console agent and MCP propose allowed ops. A person approves.

  • Zenith Editor ships

    Approved code work opens as a PR, gets AI review, and can deploy to dev. Reverts feed the learning loop.

Deploy

Built for regulated estates — and the networks they require

On-prem, private cloud, or customer cloud. Same substrate: verification, memory, and audit that complete inside your boundary.

  • Layer, don’t rip-and-replace

    Sits above EMR, LIMS, ERP, and the stacks you already run. Reads through existing interfaces. Source systems keep running if the layer pauses.

  • Judgment off the model path

    The decision path is deterministic rules and ontology queries — not an LLM. Air-gapped sites keep judgment quality even when language models stay small and on-prem.

  • Standards you can leave with

    Knowledge, constraints, and provenance sit on international standards (RDF, OWL, SHACL, SPARQL, PROV-O). Your ontology is portable — not trapped in a proprietary graph format.

  • Audit as architecture

    Signed verdicts, immutable safety charters, and full promotion history. Explanation and documentation are produced at judgment time — not reconstructed after the fact.

Console

Ops work, handled end to end

The same trust model powers day-to-day operations: registered actions, preview, approval, restore — on the ontology Zenith maintains.

  • Runs only defined actions

    No arbitrary SQL. New actions are registered by humans, not invented at runtime.

  • Preview before execution

    See row impact and a sample result. Nothing writes to prod without a second confirmation.

  • One-click restore

    Bad executions roll back from a saved prior state. Who changed what is fully logged.

  • Operate from chat or MCP

    Ask in the console, or connect Claude Desktop, Cursor, and other MCP clients you already use.

  • Learns your domain

    Connect a repo and database. Zenith introspects the schema and drafts ops actions against your model.

  • Incident triage

    Monitoring alerts become incident cards with similar cases, candidate fixes, and affected scope.

  • Decision separated from execution

    AI proposes. Authorized roles approve. Systems execute. Concurrent admins do not silently overwrite each other.

  • Code changes in the same cycle

    Beyond data fixes: open PRs, AI review, and dev deploy for small feature and query changes.

Next

Put verification under every decision

Start in the console, or talk to Automata about embedding Zenith above the systems you already run.

Open Zenith