Predictable Machines builds software verification tools that help development teams ensure AI-generated and human-written code is trustworthy, accountable, and reliable. Its flagship product, Predictable Code, combines formal verification techniques with large language models to detect code deviations and explicitly report assumptions and limitations. The platform supports multiple programming languages and is designed to handle the review bottleneck created by AI coding assistants.
Funding
Funding not disclosed
Founders
Product
Problem
AI code assistants such as Claude Code, OpenAI Codex, and GitHub Copilot enable developers to produce code at unprecedented speed, but this creates a critical bottleneck: more code is being generated faster than it can be sustainably reviewed. AI-generated code can be confidently wrong in subtle ways—appearing correct and passing tests while silently mishandling edge cases or misinterpreting intended behavior—and these risks compound as more code is produced. Traditional verification approaches are often too rigid or opaque to address this new reality.
Solution
Predictable Machines provides a software verification platform called Predictable Code that helps development teams build trust in their software systems. The platform integrates formal verification techniques, such as theorem proving, with approximate methods like large language models to verify code across a wide variety of programming languages. Rather than delivering false confidence, Predictable Code explicitly reports the assumptions, approximations, and potential limitations encountered during verification, empowering users to make informed decisions about their code's correctness. The platform distinguishes between pure code, code with unknown side effects, and code with well-understood side effects, applying appropriate verification strategies for each case. It is designed to never confidently yield false positives or false negatives, instead expressing uncertainties and alerting users when it cannot reliably guarantee correctness. This approach makes verification results honest and actionable, whether for routine development or critical systems where correctness is non-negotiable.
Target Audience
Primary users are software development teams and engineering organizations that rely on AI code assistants and need to verify code correctness, particularly those working on critical systems where reliability is non-negotiable, as well as enterprises in regulated industries such as banking and finance.
Features
- Hybrid verification engine combining theorem proving for precise semantic modeling with large language models for approximate analysis
- Explicit reporting of assumptions and approximations in verification results, ensuring transparency about what could compromise correctness
- Support for a wide variety of programming languages within a single verification platform
- Side-effect-aware verification that models database interactions, external calls, and other effectful operations with clear documentation of modeling limitations
- Guarantee against confident false positives and false negatives, with uncertainty alerts when reliable verification is not possible
- Designed to detect code deviations in real-world scenarios, as demonstrated in banking project case studies