OntologyOS · The Compliance Substrate

The semantic layer
your auditor can read.

Most AI tools can read your database. OntologyOS lets agents reason over your control schema — the classifications, the policies, the safeguards, and what they mean when a restricted-data record enters an approval chain.

The Problem

Your tools see columns.
Your auditor needs context.

A schema says classification = "restricted". That's a string. OntologyOS understands that "restricted" means AES-256 encryption at rest, no cross-region transfer without CISO sign-off, and a SOC 2 CC6.7 control obligation. That's evidence.

Without that context, agents take technically valid actions that fail a compliance review. They see a row. They don't see the control obligation attached to it. OntologyOS closes that gap.

See it in action →
Without OntologyOS

"Action: export record to us-east-2. Classification field: 'restricted'. No matching API rule. Proceeding with export."

With OntologyOS

"Blocked: restricted-classification record cannot transit to a non-BAA region. Routing for CISO approval per SOC 2 CC6.7."

How It Works

Define. Connect. Reason.

Three steps from your existing integrations to agents that reason like your domain experts.

01

Define your domain

Auto-discover your entities, relationships, and business rules from the systems you connect — CRM, ERP, HR. Customers, invoices, cases, employees, whatever your business runs on. The canonical model surfaces from your real data, refined by execution.

02

Connect to your data sources

OntologyOS maps your domain model onto your real systems — Salesforce fields, database columns, API responses. Automatic field mapping with zero manual configuration.

03

Let agents reason over it

Vairity platform agents now understand your business context. They route decisions based on rules, not guesswork. Audit trails explain decisions in plain English, not JSON.

ontology.yaml — Compliance Control Schema
# OntologyOS — shared by BUILD, AUTOMATE, CONTROL
version: "1.0"
framework: [soc2, hipaa, nist_800_53]

entities:
  DataAsset:
    properties:
      - classification: [public, internal, confidential, restricted]
      - residency: region
      - contains_phi: boolean
    controls:
      - restricted_encryption: aes_256_at_rest
      - phi_baa_required: true
      - cross_region_above_internal: ciso_approval

  ChangeRequest:
    status: [draft, submitted, approved, deployed]
    approval_chain:
      - internal: peer_review
      - confidential: security_review
      - restricted: ciso_plus_legal

# Agents reason over your control schema.
# Not just your database schema.
What OntologyOS Unlocks

Four things no other
enterprise AI tool does.

01

Map data to control schemas

Source fields are auto-classified — public, internal, confidential, restricted — and propagated across systems. Agents understand sensitivity, not just shape.

02

Route on policy, not code

Cross-region transfers of confidential data require CISO sign-off. PHI exports require a BAA. Restricted change requests require legal review. Defined once, enforced everywhere — across BUILD, AUTOMATE, and CONTROL.

03

Audit trails your auditor can read

OntologyOS produces evidence in plain language: "Blocked: PHI export to non-BAA region per HIPAA §164.502." Not a 200-line JSON trace your auditor has to interpret.

04

Evolves with your control environment

New SOC 2 control. New HIPAA safeguard. New internal policy. Update the ontology — every BUILD app, AUTOMATE workflow, and CONTROL policy picks it up. No retraining. No engineering sprint.

05

Anomaly detection across your control schema

Because OntologyOS knows what a normal action looks like under your control framework, agents surface anomalies — unusual approval chains, out-of-policy classifications, data flows that would fail a SOC 2 walkthrough — that schema-level tools would miss.

Deep Differentiation

Few platforms combine semantic modeling
with governed agent execution at enterprise depth.

Security-focused AI governance tools don't model your business domain. ITSM automation tools lock their ontology to IT service management. General workflow tools have no semantic layer at all.

OntologyOS is a horizontal semantic layer you define — connected to your systems, reasoning over your business logic, and shared across BUILD, AUTOMATE, and CONTROL.

Compare platforms →
Capability OntologyOS Workflow tools Dev frameworks
Entity & relationship modeling
Business rule engine
Auto field mapping across systems
Human-readable audit trail
Domain evolves without rebuild
Roadmap

OntologyOS v2
roadmap priorities.

Phase 2 of the Vairity roadmap brings a visual domain modeling studio — build your ontology with a drag-and-drop UI, no YAML required. Coming soon for early-access design partners.

Near-term

Ontology Studio

Visual, drag-and-drop domain modeling UI. Define entities and rules without touching YAML.

Near-term

Agent Memory Store

Long-term knowledge store per agent. Domain knowledge persists across executions and improves over time.

Following

Domain Analytics

Pattern detection, anomaly reporting, and ROI measurement directly tied to your business ontology.

OntologyOS · Early Preview

Ready for AI that
gets your business?

Join the waitlist to Vairity BUILD with OntologyOS. Self-hosted, no vendor lock-in.

Self-hosted deployment · No vendor lock-in · Enterprise agreements available

FAQ

Questions about OntologyOS

What is OntologyOS?

OntologyOS is Vairity's semantic domain modeling layer. It auto-discovers your business entities from the integrations you connect, and refines a single canonical model as workflows run. Every Vairity agent reasons over that model, so when an agent touches a "Customer," it understands your company's definition, not a generic database schema.

How is this different from a standard data schema?

A database schema describes structure. OntologyOS encodes meaning — business rules, relationships, approval thresholds, routing logic, and exception conditions. An agent using OntologyOS can answer "should this invoice be escalated?" using your rules, not generic LLM guesswork.

Do I need to retrain an AI model when my business rules change?

No. OntologyOS sits above the model layer. When your business rules change — a new approval tier, a renamed field, a new product line — you update the canonical model. All agents automatically reflect the change on next run. No retraining, no fine-tuning, no model redeploy.

What happens when a connector field name doesn't match my ontology?

OntologyOS handles auto field mapping. If Salesforce calls it AccountName and your internal CRM calls it ClientTitle, OntologyOS maps both to your canonical Customer.name. Agents never see the inconsistency, and you stop writing brittle prompt engineering just to align field names.

Is OntologyOS available standalone?

OntologyOS is the intelligence layer built into the Vairity platform. It is not currently available as a standalone product. The Ontology Studio — a visual editor for your domain model — is on the near-term public roadmap.