We help AI-first companies in San Francisco, Silicon Valley, and India bridge the gap between laboratory benchmarks and real-world performance. Our technical lab specializes in high-fidelity model training data, SFT traces, and production reliability evaluation.
Specializing in LLM behavioral analysis, bias detection, and production-grade validation across live enterprise workflows.
Generic benchmarks fail in production. We build evaluation frameworks for the industries that matter most.
RAG reliability testing, hallucination suppression, and workflow agent evaluation for San Francisco's leading SaaS platforms.
Adversarial training and safety audits for financial agents. Ensuring compliance with US and Global financial AI standards.
Specialized training data for vision-language models and medical diagnostics. High-precision evaluation for high-stakes AI.
Analyzing consistency, edge cases, and regression patterns across actual user scenarios rather than static datasets.
Surfacing hidden failure modes and performance decay that often emerge only during sustained operational usage.
Translating complex model behaviors into clear, reproducible, and actionable insights for product and engineering leads.
We evaluate AI systems where they live—inside SaaS platforms, developer tools, and enterprise environments—identifying issues missed by standard QA.
Our reports focus on technical root causes. We help you understand exactly why a model fails, enabling rapid engineering iterations.
We focus on predictability. Our testing ensures that your AI remains a stable, trusted component of your product stack over long horizons.
As an external partner, we provide the objective verification required for enterprise-grade adoption and high-stakes deployments.
A rigorous, structured framework for evaluating the production readiness of frontier AI systems.
Mapping user journeys and system architecture to define high-impact evaluation scenarios that mirror actual production usage.
Simulating sustained interactions—long sessions and complex prompt sequences—to evaluate stability and logic preservation over time.
Detecting hallucinations, security vulnerabilities, and logic drift that standard unit tests and benchmarks frequently fail to capture.
Understanding our evaluation protocols and how we integrate with your engineering lifecycle.
Uncover hidden reliability gaps, behavioral drift, and trust issues before they impact your revenue.