Evalium builds evaluation-first human data: model-breaking evals, RL environments, and expert datasets for frontier AI — across coding, STEM, professional reasoning, computer use, agentic workflows, and embodied AI.
Frontier labs don't need more data. They need the right data — targeted at the exact capabilities their models lack. That targeting is impossible without rigorous evaluation, and it's why so much human data underdelivers: the evaluation came last, if it came at all. We built Evalium the other way around. Evaluation isn't our QA step. It's where every engagement begins.
Expert-designed tasks calibrated to the difficulty band where models fail. Pass@k calibration ensures every task produces signal, not noise.
Our agentic evaluators re-run and perturb failing trajectories to trace each failure to its root cause — not just where the model breaks, but why.
Our experts produce the demonstrations, trajectories, environments, and preference data that target those gaps. Nothing generic. Everything aimed.
Every dataset ships with its own eval: acceptance rubrics, verifier results, and held-out QC samples — delivered alongside the data, not promised after it.
Private, uncontaminated evaluation suites for frontier models: rubric design, LLM-as-judge pipelines, agentic evaluation with root-cause analysis, and expert graders in the loop.
→ evals & benchmarksExpert demonstrations, SFT and preference data, and long-horizon agentic trajectories — built to target the failure modes our evals surface.
→ model-breaking dataTool-based and UI-based gyms with structured task design, reward configuration, and programmatic verifiers, calibrated to where current agents break.
→ rl environmentsRepo-level tasks, code review, issue resolution, terminal agents, long-horizon SWE workflows.
Math, physics, biology, and chemistry at graduate and research level, with verifiable answers.
Finance, legal, consulting, and operations workflows performed by vetted domain experts.
CUA trajectories, mock apps and websites, DOM-level annotation, cross-OS harness at scale.
Function calling, multi-turn planning, error recovery, trajectory-level agent evaluation.
Teleoperation data, VLA training data, physical-task evaluation.
Built by a team that has designed, sold, and delivered frontier data programs for the world's leading AI labs.
Grounded in peer-reviewed research at ICSE, ESEC/FSE, and MSR.
Send us a capability you care about. We'll come back with a sample eval — tasks, rubric, and a first read on where the model breaks.
Request a sample eval