Reasonable is developing training paradigms for ultra‑advanced coding large language models that can master programming paradigms beyond human capability. By grounding these models in formal verification, they aim to create AI that reasons software into existence rather than writing it manually, targeting complex, high‑assurance codebases.
Funding
Funding not disclosed
Founders
Product
Problem
Developing software for safety‑critical systems often requires reasoning about complex invariants and formal correctness, tasks that exceed the capacity of typical development teams and lead to costly errors and delays.
Solution
Reasonable creates training frameworks for ultra‑advanced coding large language models (LLMs) that are capable of mastering programming paradigms beyond human expertise. By anchoring these models in formal verification techniques, the company enables AI to generate code that is provably correct rather than writing it line‑by‑line. The approach combines superhuman LLM performance with mathematical guarantees of correctness, allowing the creation of software for domains where reliability is paramount. This reduces the need for extensive manual verification and accelerates development of complex, safety‑critical codebases.
Target Audience
Primary customers are organizations building safety‑critical software, such as aerospace, automotive, medical device, and critical infrastructure firms that require formally verified code.
Features
- Specialized training pipelines that expose coding LLMs to formal verification constraints during learning
- Integration of theorem‑proving and model‑checking methods to enforce provable correctness of generated code
- Support for programming paradigms and language features that are currently infeasible for human developers to manage at scale
- Architecture designed to produce code with built‑in correctness certificates, facilitating downstream validation
- Scalable infrastructure for fine‑tuning superhuman LLMs on domain‑specific safety‑critical software repositories