Axiomatic AI
Axiomatic AI develops Axiomatic Intelligence (AxI), a verifiable AI system that combines formal methods, physical laws, and known results to produce trustworthy outputs with machine-checkable guarantees. Its web-based platform, Lemma, works alongside existing scientific workflows, allowing users to direct the approach while the AI performs managed, verified work. The system validates results against measured data and physics-based rules, and has demonstrated the ability to find 89.7% of known errors in corrected mathematics and CS papers.
- Artificial Intelligence
- AI Agents
- Software Only
- Data & Analytics
Funding
Founders
Founder details are not available yet.
Product
Problem
Scientists and engineers face a growing verification gap as AI-generated code and scientific text proliferate—traditional review processes cannot reliably validate the correctness of AI outputs, especially in physical sciences where reasoning combines exact mathematics with approximations, modeling choices, and implicit assumptions. This limits the trust and adoption of AI in research and design workflows where "seems good enough" is not acceptable.
Solution
Axiomatic AI develops Axiomatic Intelligence (AxI), a verifiable AI system that combines formal methods, physical laws, and known results to produce trustworthy outputs with machine-checkable guarantees. Its web-based platform, Lemma, works alongside the tools and workflows scientists already use, allowing users to direct the approach and approve plans while the AI performs the work with managed verification and reporting. The system validates results against measured data, physics-based rules, and domain benchmarks, while clearly stating assumptions and confidence ranges through uncertainty quantification. For organizations, Axiomatic AI offers private deployments integrated into existing design, engineering, and testing environments, built first for photonics to connect the full design-to-test loop.
Target Audience
Primary users are scientists and engineers in research and industrial settings who need reliable, reproducible results from AI assistance, with enterprise deployments targeting photonics and other design-to-test engineering organizations.
Features
- Formal verification using machine-checkable proofs in Lean, with autonomous formalization of textbook-scale physics material (1,019 proof tasks in the AxQM benchmark from Nielsen and Chuang)
- Automated simulation and analysis capabilities, demonstrated by integrating point vortex systems with RK4 and checking against conserved quantities
- Paper validation that detects errors in equations, proofs, claims, and references, finding 89.7% of known errors in 26 corrected mathematics and CS papers and 84% in 50 recent physics papers
- Uncertainty quantification that explicitly states assumptions made in the reasoning and the range of confidence in each result
- Private deployment options tailored to organizational tools, workflows, and knowledge, with integration into existing engineering and testing environments
- Data ownership protection: no use of private work for model training, cross-user evaluation, or capability improvements without explicit opt-in