Custom benchmarks
Reasoning, agentic, and domain-specific tasks designed around the capabilities that matter to your team.
A data research lab for AI
Make model
progress legible.
Datasets, benchmarks, and evaluations that help AI teams train better models and understand where they fail.
Explore our workBetter measurement. Clearer conclusions.
Progress deserves
rigorous evidence.
We build statistically sound evaluations and high-quality training data for frontier AI labs, applied-AI startups, and enterprises shipping LLM products. Our focus is the evidence you need to understand capabilities, uncover failures, and guide the next improvement.
Built around what
your model needs to do.
From the data a model learns from to the tasks that reveal its limits.
Evaluation suites
Rigorous evaluations to identify failure modes, compare models, and measure progress with statistical care.
Curated & synthetic datasets
High-quality training data built around the skills and domain knowledge you want your models to develop.
Statistical rigor.
Engineering that ships.
Our founding team brings together Berkeley statistics research, expertise in stochastic processes, and hands-on startup engineering experience shipping products to paying customers.
We connect careful experimental thinking with the practical work of building and evaluating AI systems.
Compute behind the research
We use NVIDIA H100 GPUs through cloud providers for synthetic data generation, running open-weight models as graders, and training small task-specific models.
As we scale our evaluation pipelines, we plan to adopt NVIDIA NeMo and Triton.
Know where your model fails.
Build what moves it forward.
Purpose-built data and evaluations for teams turning model capabilities into reliable products.
Contact us