# Ontology Labs

> Ontology Labs is a research institution establishing semantic computing as the foundational infrastructure for LLM software development. AI-first by design: AI agents write verified code, developers specify intent, existing systems integrate via FFI.

Semantic computing provides formal guarantees for bidirectional transformation preservation—ensuring that specifications, code, and execution maintain semantic equivalence with mathematical certainty. Full round-trip capability: specification to execution and back.

## Company

- [Homepage](https://ontologylabs.ai): Ontology Labs corporate website - research institution overview, proof points, and contact information
- [About](https://ontologylabs.ai/#about): What we do - AI-first semantic computing infrastructure

## Core Differentiators

- **AI-First Architecture**: AI agents are the primary developers. Humans specify intent in natural language, AI agents translate to formal specifications, guarantees flow to execution.
- **Model-Agnostic**: Works with any capable AI agent. Not tied to specific LLMs—designed for AI consumption patterns, not specific model architectures.
- **Bidirectional Transformation**: Not one-way compilation—complete round-trip. Meaning preserved in both directions with mathematical certainty.
- **No Retooling Required**: FFI bindings let you add guarantees to existing codebases. The AI decides when to use formal methods.

## Technology

- [AYIOS](https://ontologylabs.ai/#about): LLM Operating System for semantic programming. AI agents write formally verified specifications, the runtime enforces semantic integrity. Targeting 2026.
- [Semantic Computing](https://ontologylabs.ai): Infrastructure layer ensuring meaning preservation through all software transformations.

## FFI Integration

Foreign Function Interface bindings available for:
- **Python**: Async bindings with full semantic error handling
- **TypeScript**: Native bindings for Node.js and browser environments
- **Rust**: Direct integration with the core runtime
- More languages planned

Integration pattern: Add semantic guarantees to critical paths without rewriting. AI agents choose when to invoke formal verification.

## Research & Evidence

- [Patent Portfolio](https://ontologylabs.ai): 7+ provisional patent applications covering bidirectional transformation guarantees, mereological type systems, and semantic impedance matching.
- [Performance](https://ontologylabs.ai): 650x faster than traditional formal methods verification. Semantic verification built into execution, not added as a separate pass.
- [Formal Methods](https://ontologylabs.ai): Mathematical guarantees for meaning preservation across transformations.

## Contact

- [Email](mailto:hello@ontologylabs.ai): General inquiries - hello@ontologylabs.ai
- [Schedule a Call](https://calendar.app.google/fK7LSyE6k2KirjQR6): Book a meeting with the team

## Technical Foundation (Patent Portfolio)

Ontology Labs has filed patent applications covering foundational innovations in semantic computing infrastructure, including:

- Bidirectional transformation preservation in canonical semantic spaces
- Context preservation through transformation sequences with provenance tracking
- Architecture-independent verification of semantic equivalence

Additional applications cover mereological type systems, semantic impedance matching, and runtime enforcement mechanisms.

## Why AI-First?

Traditional framing: "Developers adopt constrained environment for formal guarantees."
AYIOS framing: "AI agents operate in semantically rich environment. Developers get guarantees via FFI."

The AI writes the formally verified code. Humans don't need to learn the specification language—they specify intent and integrate results. This inverts the adoption challenge: AI agents are already comfortable with formal constraints.

## Optional

- [Domain](https://ontologylabs.ai): Primary website domain - ontologylabs.ai
- [Status](https://ontologylabs.ai): Pre-product research stage, core infrastructure implemented, seeking Series A investment