The digital twin of your entire business.

The Problem

Palantir built a $50B company on one insight: enterprises are drowning in disconnected data, and the companies that can unify it win. But Palantir is for governments and Fortune 500s. What about everyone else?

Each system has its own data model, its own API, its own way of thinking about entities. Want to answer "which customers who bought product X also had support tickets that mentioned feature Y, and what was the ARR impact?" Good luck stitching that together.

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The Vision

What if your database was as flexible as a spreadsheet, as queryable as SQL, and never forgot anything?

• No migrations, ever — Adding a new attribute is as simple as... adding it. No schema changes. Just assert a new fact.
• Connect anything to anything — Every entity can relate to any other entity. No foreign key constraints holding you back.
• Query like it's one database — Traverse relationships as naturally as following links. Complex joins become simple patterns.
• Built-in time travel — Ask "what did this customer record look like last Tuesday before that import ran?" and get an answer.

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From Insight to Action

Most databases are passive. You query them, they answer. Open Ontology is different. Your ontology isn't just a model—it's a living system that knows what should exist, detects when reality diverges, and drives action to close the gap.

Entity Definitions

Define your domain with typed attributes, relationships, and validation rules. Want employees to have emails? Contracts to reference signers? The schema evolves with your understanding—no migrations, just new versions.

Compliance Rules

Write declarative rules that query for what shouldn't exist. "Onboarding employees without a signed contract." If the query returns results, you have violations. Rules run continuously or on-demand.

Tasks & Workflows

When violations are detected, automatically create tasks, assign to users or roles, and track through resolution. Your ontology doesn't just show you problems—it drives action to fix them.

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How It Works

Every fact in the system is stored as a triple: [entity, attribute, value]. No tables. No rigid schemas. Just facts about entities.

Data from different systems, unified:

Why triples?

• Schema flexibility — Add new attributes without migrations
• Sparse data — Only store what exists (no NULL columns everywhere)
• Time travel — Append-only with timestamps, query any historical state
• Graph queries — Natural traversal through references
• Unified model — Everything is an entity, everything can relate to anything

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The Action Layer

Define schemas, write compliance rules, and let the system drive resolution. Everything is declarative. Everything is queryable. Everything has history.

Schema Definition

Compliance Rule

What happens when rules run:

• Violations detected — Stored as triples in the ontology, queryable like any other data
• Tasks auto-created — Assigned to HR team, entity owner, or custom query result
• When fixed — Violations auto-resolve, tasks complete, the loop closes
• Full audit trail — Time-travel to see compliance posture at any point in history

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AI-Native Architecture

Built for the AI era. Your ontology becomes a shared language between humans and machines.

• Natural Language Queries — Ask questions in English. Get Datalog. The structured ontology makes translation reliable—AI knows your entities, attributes, and relationships.
• Schema-Aware AI — AI understands your domain. Suggests compliance rules based on your schemas. Validates data. Spots anomalies. Your ontology is the context window.
• Intelligent Forms — Forms that know what data you need, suggest values from related entities, and validate against your schema in real-time.

Example: Natural Language to Datalog

"Show me employees who started this quarter but haven't completed security training"

Compiles to:

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Who Is This For?

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Query Across Everything

Datalog is a declarative query language that makes complex relational queries feel like pattern matching. Variables that appear in multiple patterns create implicit joins.

Cross-system queries

Time travel

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Get Started

Your data. Your model. Your rules. Your workflows.

• Try the Demo [blocked]
• Learn the DSL [blocked]