Logos provides verification infrastructure that enables AI agents to operate safely in correctness‑sensitive domains. It formalises domain‑specific knowledge into machine‑checkable specifications, then a coding agent generates code that is iteratively checked by a theorem prover until all proof obligations are satisfied. The system’s Formaliser, Coding Agent, and Prover workflow ensures implementations are provably correct before deployment, reducing risk in critical applications.
Funding
Funding not disclosed
Founders
Product
Problem
AI agents are increasingly used in domains where errors can have severe consequences, such as finance, healthcare, and critical infrastructure, but there is no systematic way to ensure their code adheres to domain-specific correctness requirements.
Solution
Logos offers a verification infrastructure that transforms expert domain knowledge into formal, machine‑checkable specifications. A Formaliser component compiles this knowledge into a locked specification, which a Coding Agent uses to generate implementation code. A Prover then iteratively checks each proof obligation against the specification, providing feedback until the code is mathematically proven correct. This loop enables AI agents to be deployed in correctness‑sensitive environments with provable guarantees of functional correctness.
Target Audience
Primary customers are organizations that develop or deploy AI systems in high‑risk sectors such as finance, healthcare, aerospace, and critical infrastructure, where formal correctness guarantees are required.
Features
- Formaliser that converts domain expertise into immutable formal specifications
- Coding Agent that automatically generates code constrained by the formal specification
- Integrated theorem prover that validates proof obligations and returns verifiable feedback
- Iterative verification loop ensuring the implementation meets the specification before deployment
- Support for multiple correctness‑sensitive domains through customizable knowledge templates