ONTOLOGY GOVERNANCE FOR AI

Knowledge your AI can use.
Evidence your people can trust.

ONTO turns the technical and functional evidence you already have into governed, reviewable ontology knowledge. It gives AI agents a clear operating boundary: what is approved, what it means, and where it came from.

Discuss an ONTO pilot Explore the factory

01: Canonical ontology model
03: Connected product capabilities
0: Uncontrolled source writes

ONTO Governance Factory: Atlas assesses source evidence, Nexo validates an ontology release, and Argos provides trusted AI runtime answers. — From scattered organizational data to governed, AI-ready knowledge.

Enterprise data is not automatically AI-ready.

The missing layer is governed meaning.

THE ONTO FACTORY

A disciplined path from evidence to answer.

ONTO keeps assessment, approval, and consumption intentionally separate. That prevents early discoveries from quietly becoming official definitions, and prevents an AI runtime from stepping beyond the approved release.

01 / ATLAS

Readiness assessment

Atlas maps the available technical and functional evidence for a domain, identifies gaps, and produces a reproducible baseline.

Inventories metadata, models and documentation
Preserves source hashes and evidence references
Scores readiness and exposes the gap backlog

OUTPUT: ASSESSMENT PACKAGE

02 / NEXO

Registry and validation

Nexo turns evidence into reviewable candidates, records decisions, and emits an approved, reconstructible ontology release.

Separates candidates from approved knowledge
Captures human review, authority and rationale
Creates context packs and publication-ready mappings

OUTPUT: ONTOLOGY RELEASE

03 / ARGOS

Trusted runtime

Argos investigates an approved release with evidence, controlled read-only operations, and explicit abstention when limits are reached.

Uses only approved concepts, bindings and sources
Never accepts free-form SQL from a question
Explains when evidence, permission or detail is missing

OUTPUT: EVIDENCE-GROUNDED ANSWER

ARCHITECTURE WITHOUT LOCK-IN

Two planes. One governed contract.

Where data lives and where ontology knowledge is governed are different decisions. ONTO makes the boundary explicit, so your existing platform choices remain yours.

DATA PLANE

Read metadata and authorized operational data.

Fabric, Databricks, Snowflake, SQL Server, MySQL and semantic models can contribute metadata, evidence or governed read-only results.

FABRICDATABRICKSSQLSEMANTIC MODELS

ONTOLOGY PLANE

Govern what knowledge means and who approved it.

ONTO maintains a canonical release with evidence, decisions and mappings. It can prepare packages for external systems without turning them into automatic authorities.

CANONICAL JSONJSON-LD / RDFEXTERNAL MAPPINGS

Argos runtime displaying an answer with its evidence and source references.

THE FOUNDATION OF TRUST

AI can suggest.
People and rules decide.

ONTO does not turn an LLM inference into an official definition. It keeps evidence, confidence, human review and authority visible from the first assessment to the final runtime response.

Evidence over inferenceEvery approved element traces back to a source or human decision.

Human-in-the-loopReview is a product capability, not a manual afterthought.

Abstention by designArgos says when a question is outside the approved evidence.

PILOT EVIDENCE

Prove the approach in a bounded domain.

ONTO is delivered today as a local, single-application pilot. The objective is practical: validate a governed knowledge path before committing to a larger operating model or external publication.

Plan a scoped ONTO pilot

33: tables inventoried in a validated schema-first case
451: traceable ontology candidates produced for review
5: controlled, read-only operational query templates
6/6: passed runtime evaluation cases in the Fabric pilot

CLEAR BY DESIGN

What ONTO does not claim to be.

Not a replacement for your data platformNot a replacement for your catalog or data governanceNot an autonomous ontology generatorNot an external publishing engine in the current MVP

START WITH THE EVIDENCE YOU HAVE

Build the governed knowledge layer before asking AI to reason over your enterprise.

Talk to Over55IT

Knowledge your AI can use.Evidence your people can trust.