Hallucinations, prompt drift, safety bypasses. I help AI startups find and fix the reliability problems that slip past standard QA.
Your model gives wrong answers with perfect confidence. Users trust it. That's the dangerous kind.
You tweaked the system prompt to fix one thing. Three other behaviors broke. You found out in production.
Your safety layer blocks the obvious attacks. But a few clever rephrases get through every time.
Same input, different outputs. Your downstream logic can't handle it. Neither can your users.
You have unit tests. You have integration tests. But who's testing what the model actually says?
GPT-4o dropped. Claude 3.5 dropped. You upgraded and something important quietly broke.
I'm not a big agency. I'm Avinash, a testing specialist who works directly with AI startup founders and engineers to build confidence in your LLM-powered products before they reach users.
Structured adversarial testing, edge case cataloguing, and systematic prompt stress-testing to surface what breaks before launch.
Not just a one-time audit. I help you build repeatable eval pipelines so your team can test every model update independently.
Safety vs. helpfulness. Precision vs. recall in outputs. I've reasoned through these tradeoffs across multiple AI products.
Structured reports with severity ratings, reproduction cases, and recommended fixes in language your team and investors understand.
Systematic testing of model outputs against quality, accuracy, and safety benchmarks tailored to your use case.
Learn moreRigorous testing across data quality, robustness, bias, metrics, and explainability for machine learning models heading to production.
Learn more30-minute free call. No pitch. Just a conversation about what you're building and where it might break.
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