Now in Early Access

Your Data Needs an
Ontology to Think with AI

OntologySim is the enterprise data simulation platform that lets teams model, enrich, and run AI pipelines over structured ontology graphs — no PhD required, no months of setup.

Launch the Simulator → See how it works
ontologysim · live ontology graph · v3.5
Entity Patient Loan Pipeline Engine Risk Score Insight AI Agent Output
3
Enterprise Domains
30+
Pre-built Pipelines
10×
Faster Prototyping
100K+
Synthetic Records Generated
v3.5
Platform Version · Live
The Problem

AI is only as smart as your data model

Every organization races to deploy AI — but most hit the same wall: raw data without structure is useless to a language model. You need entities, relationships, semantics. You need an ontology.

Building ontologies from scratch takes months. Testing pipelines against real data means compliance risk. Demonstrating value to stakeholders requires a live, queryable system — not a whiteboard.

OntologySim solves all three. We give you a fully operational ontology simulation environment — pre-modelled with real-world enterprise domains, synthetic data at scale, and AI pipelines ready to run in minutes.

🏗️

Months of Setup, Compressed to Minutes

Spin up a fully wired ontology environment with entities, relationships, and pipeline logic — without writing a single line of schema code.

🔒

No Real Data Risk

All simulation data is synthetically generated and statistically realistic — so teams can test freely without PII, compliance, or breach exposure.

🎯

Demo-Ready from Day One

Share a token link with your stakeholder. They see a live, domain-specific data platform — not slides.

🤖

Built for the AI Era

Pipelines feed directly into AI insight and agent layers — so your ontology becomes the thinking backbone of your AI system.

Why Ontology

Structure is the new competitive moat

In the AI era, the organizations that win are those whose data has meaning. Ontologies provide the semantic layer that turns raw tables into knowledge graphs — the foundation every LLM, agent, and pipeline needs to reason accurately.

🕸️

Entities & Relationships

Define real-world objects — Patient, Transaction, Loan — and the typed links between them. An ontology is not a schema. It's a world model.

Foundation Layer

Pipeline Enrichment

Pipelines traverse the graph — joining entities, computing derived metrics, and writing enriched objects back to the ontology in real time.

Compute Layer
💡

AI-Ready Semantics

Language models and agents don't work well on CSV files. They work well on structured, typed, contextualized knowledge. Your ontology is that context.

Intelligence Layer
Domain Coverage

Built for the industries that matter most

OntologySim ships with three deep-domain ontology models — each with realistic entities, relationships, pre-built pipelines, and AI agent integrations.

Healthcare Data Platform

Model a complete clinical data environment — from patient intake to lab anomalies, treatment outcomes to insurance reconciliation. Run risk scoring pipelines and let AI agents surface the patients who need immediate attention.

PatientPhysicianTreatment LabResultInsuranceClaim
  • Patient Risk Scoring — joins patients + treatments to compute composite risk_score
  • Lab Anomaly Enrichment — detects consecutive abnormal results and triggers care gap alerts
  • 30-Day Readmission Risk — scores discharged patients using age, outcome, and critical labs
  • Insurance Reconciliation — matches claims to treatments and flags approval gaps
  • Physician Workload Analysis — aggregates patient load and flags burnout signals
healthcare · ontology objects
🧑 Patient 120 records
└─ joined via treatment_id ──▶
💉 Treatment 216 records
└─ enriched with ──▶
🧪 LabResult 375 records
└─ pipeline output ──▶
✦ risk_score · care_gap_registry AI-ready

Payments & Fraud Platform

Simulate a complete transaction graph — merchants, customers, authorizations, chargebacks. Run fraud scoring, AML screening, and velocity anomaly detection. Surface SAR candidates before regulators do.

MerchantCustomerTransaction ChargebackAuthorization
  • Fraud Score Engine — joins transactions + chargebacks to produce weighted fraud_score
  • AML Transaction Screening — applies structuring & layering typology rules, flags SAR candidates
  • Velocity Anomaly Detection — flags >5 transactions/hour or >$10K per 24h per customer
  • Geographic Risk Heatmap — maps fraud rate z-scores by region and channel
  • Customer Risk Profiling — 90-day aggregation assigns risk tier per customer
payments · ontology objects
🏪 Merchant 72 records
└─ linked via merchant_id ──▶
💰 Transaction 480 records
└─ enriched with ──▶
⚠️ Chargeback 64 records
└─ pipeline output ──▶
✦ fraud_score · aml_alert_queue AI-ready

Mortgage Servicing Platform

The most comprehensive mortgage ontology available — 10 object types covering the full loan lifecycle from origination to delinquency resolution, investor reporting, and regulatory compliance.

BorrowerLoanProperty InvestorDelinquencyLossMitigation ComplianceCase
  • LTV Risk Classifier — computes LTV, flags high-risk borrowers, assigns investor pool
  • Delinquency Detection — flags 2+ missed payments, assigns DPD stage
  • Portfolio Stress Test — simulates +200bp rate shock and -20% property decline
  • HMDA Regulatory Reporting — builds LAR from Loan + Borrower, validates 110 required fields
  • Compliance Case Tracker — monitors CFPB, TILA, RESPA, Fair Lending violations
mortgage · ontology objects
👤 Borrower + 🏡 Property 96 + 96
└─ underwritten into ──▶
📄 Loan + 📊 Investor 120 + 24
└─ monitored via ──▶
⚠️ Delinquency → LossMitigation 48 + 36
└─ pipeline output ──▶
✦ loan_risk_scores · hmda_submission AI-ready
Platform Features

Everything a modern data team needs

OntologySim is not just a demo tool. It's a full-stack data platform simulator — built to show real architecture, real pipelines, real AI.

🕸️

Ontology Manager

Define, browse, and manage entity types, properties, and relationships in a visual Mermaid graph. Understand how your data objects connect before you build.

Pipeline Engine

30+ pre-built pipelines per domain that join, enrich, and derive computed metrics. Run them on-demand or auto-trigger after data generation.

⚖️

Scale & Export

Generate 1× to 10,000× synthetic records in seconds. Export full domain state as JSON, share with teammates, and reload at any time.

📈

Analytics Dashboard

Domain-specific charts — risk distributions, fraud heatmaps, LTV breakdowns, delinquency stages. Powered by Chart.js with live pipeline data.

🔧

Pipeline Builder

A drag-and-explore interface to see every pipeline's logic — sources, transforms, outputs — and understand how data flows from raw to enriched.

Transform Library

Browse and read 15+ SQL and Python transform definitions per domain. Use these as blueprints for your real data engineering implementation.

🖥️

Operational Workshop

A live case management view — exception queues, entity cards, and action buttons. Demonstrates exactly how a frontline analyst would interact with enriched data.

Validate & Import

Import external JSON datasets and validate them against the ontology schema before pipeline processing. Catch schema drift early.

🗂️

Token Access Control

Generate domain-scoped access tokens in seconds. Share a single link with a client or colleague — they see only what you intend them to see.

What's Next

Evolving into the AI Agent Data Simulator

OntologySim is building toward something bigger: a fully autonomous data simulation environment where AI agents don't just read the ontology — they extend, query, and act on it. The platform is the training ground for your future AI workforce.

Agent Execution Pipeline · Coming Soon
01
Ontology-Grounded Context

Agents receive a semantic snapshot of the ontology graph — entities, relationships, live pipeline state — as structured context.

02
Autonomous Pipeline Orchestration

Agents decide which pipelines to run, in which order, and with what parameters — based on the current state of the data graph.

03
Write-Back & Mutation

Agents write new derived objects and relationships directly into the ontology — closing the loop between AI reasoning and structured data.

04
Multi-Agent Collaboration

Specialist agents — Risk, Compliance, Operations — coordinate over the shared ontology, each contributing domain-specific enrichments.

Data Generation Agent

Instructs the simulator to generate, scale, and shape synthetic datasets based on natural language objectives — "simulate a mortgage portfolio stress scenario with 20% delinquency."

In Development
Pipeline Architect Agent

Given a business question, the agent designs a new pipeline — selecting sources, proposing transforms, and registering the output in the ontology. Human approval keeps oversight intact.

In Development
Insight & Anomaly Agent

Continuously monitors pipeline output for statistical anomalies, generates natural language reports, and surfaces prioritized action queues to the operations team.

Planned · Q3 2026

From zero to live ontology in 4 steps

1

Request Access

Get a domain-scoped access token. Click your link — no signup form, no onboarding sequence. You're in.

2

Generate Your Data

Choose your scale factor — 1× for prototyping, 1000× for stress testing. Synthetic data is generated in seconds.

3

Run Pipelines

Execute pre-built pipelines that join, enrich, and derive metrics across your ontology objects. Watch the graph come alive.

4

Explore with AI

Use the AI insight engine and agent triage to interrogate your data — ask questions, surface risks, and act on recommendations.

"The organizations that succeed with AI in the next decade won't be the ones with the best models.
They'll be the ones with the best-structured data."

— The OntologySim Founding Principle

Get Started

Ready to see your data with structure?

OntologySim is available now in early access. Launch the platform, generate your domain, and experience what an AI-ready ontology actually feels like — in minutes, not months.

Launch the Simulator → Talk to the Team

Need a domain access token? Contact us at support-ontology@etlbyte.io and we'll generate one for you.