Crash-Test Dummies for AI: Testing Chatbots Across Languages and Cultures

Circuit Breaker Labs has been named one of TechCrunch's 2026 Startup Battlefield 200 finalists for work on making AI safer across languages and cultures. TechCrunch

The company was founded by siblings Shirali Nigam, its chief executive, and Arul Nigam, its chief technology officer.

At the center of its approach are AI agents the company compares to an army of crash-test dummies. The agents mimic people of all ages, backgrounds, languages and cultures to test whether models catch dangerous, psychologically harmful interactions.

Built with human domain experts, the agents form highly realistic user simulations. The simulations are used for red-team tests, controlled adversarial attacks designed to expose model failures.

The company reports running tens of thousands to hundreds of thousands of simulated interactions per day.

It operates as a safety testing lab for high-risk AI uses such as AI coaching, journaling, or other mental health support apps. It grades model behavior with a proprietary scoring method to produce auditable, explainable safety scores.

Its stated position is that “it independently stress-tests AI to find dangerous failures so real people never do.”

The broader context here is a shift from functional evaluation to behavioral safety evaluation. Accuracy benchmarks measure whether a model answers correctly. Safety testing for coaching and support measures whether it stays safe across long, emotionally loaded, multilingual interactions where harm is gradual rather than explicit.

In my view, the hard engineering is cultural coverage and making results actionable. Personas that vary by age, language and cultural context can probe guardrail consistency in ways single-locale prompt sets cannot. Value depends on simulation fidelity, on how well synthetic users copy rephrasing, persistence and indirect expressions of distress, and on whether scoring traces a failure to a reproducible interaction engineers can fix.

Looking at what this means for teams shipping high-risk companions, independent high-volume simulation changes the testing loop. Internal evaluators know the system prompts and refusal logic. External personas do not. That distance is useful. That volume also allows regression testing after model, system-prompt or policy changes, rather than one-time review before launch. Auditable and explainable scores matter because product, safety and legal reviewers need a common record, not only a pass or fail rate.

Worth flagging in that light is where this points if it works at production scale. Crash testing did not eliminate vehicle risk, but it created a repeatable method for finding failure before deployment. Used consistently for AI coaching and journaling, multilingual adversarial simulation could give builders a practical way to test to failure, fix, and retest before real users meet the same breakdowns.