Create mutants
Flip operators like + to − or ≥ to ≤ to create realistic faults in the codebase.
High‑quality, predictable unit tests powered by mutation testing and AI agents.
Stop brittle, hallucinated tests and make quality measurable: break the code on purpose, see what survives, and have an AI agent write the exact tests needed to kill the mutants—at cloud scale.
How it works
Mutation testing reveals blind spots by introducing small code changes and checking whether tests catch them; AI then generates targeted tests to close the gaps, and the whole loop runs massively in parallel.
Run current tests
Execute the existing test suite to see which mutants survive—these mark missing or weak tests.
AI closes gaps
An AI agent writes precise new tests that are validated by re‑running the mutation cycle.
Scale out
Thousands of mutation runs execute in parallel on an autoscaling compute cluster.
Ship with confidence
Achieve predictable, enterprise‑grade test quality with measurable coverage against real faults.
The problem
- Developers want AI to write tests, but quality is poor and non‑deterministic.
- Hallucinations creep in and test quality collapses when scaling across large codebases.
- Human review remains the bottleneck, increasing technical debt on long‑running projects.
Our solution
- AI agent guided by mutation testing delivers guaranteed, predictable test quality.
- Measurable feedback loop: only ship when surviving mutants are driven to zero.
- Built to handle enterprise‑scale projects with distributed, parallel execution.
Why now
AI can produce code cheaply and quickly, but it often breaks existing behavior; mutation testing provides the right metric and feedback to scale reliable test generation, where “just ask AI to write tests” simply doesn’t work.
The product
- An IDE extension adds a button that instantly covers new code with validated unit tests.
- Under the hood, the AI agent invokes the mutation engine to decide exactly which tests to write.
- Backed by a large autoscaling compute cluster for fast, parallel mutation runs.
Key benefits
- Fewer regressions and safer refactors.
- Deterministic quality instead of LLM guesswork.
- Massively parallel throughput for big teams.
Market size
IT teams spend about $340B per year writing unit tests, driven by ~47M developers, ~52% writing tests, ~20% time on tests, and an average $70k salary; capturing even 10% is a ~$34B opportunity.
Competition
- No direct solution offers AI + mutation testing at scale with seamless test generation.
- Python space: open‑source tools like mutmut and Cosmic Ray lack distributed scale and AI feedback.
- Other languages: Arcmutate/PIT, Stryker, VisualMutator, Gambit exist, but not as an AI‑guided, parallel system.
- Services and AI test startups focus on staffing or pure LLMs; mutation‑driven feedback wins on quality.
Go‑to‑market
Jan 2026
Open‑source Python core
Launch and start promo to attract early adopters and contributors.
Apr 2026
First 1,000 users
Grow via product‑led acquisition and community channels.
Summer 2026
SaaS + Agent
Release the hosted platform and begin full agent rollout.
Founder
Evgeniy Blinov
- 7+ years in software engineering and team leadership (12‑person team lead experience).
- Worked at leading tech companies: Yandex, Avito, Ozon, Sber, VK.
- Author of ~30 Python libraries and frequent conference speaker.
