Your agent is only as good as your evals
Every agent has a different job. We measure models, tools, and configurations against your agent's workload.
Work with us
Tell us what you're shipping. We'll show you which model performs best on your workflows.
How it works
We can rightsize the evals process for you, based on your unique needs. We'll help with scoping to grading, setting up reinforcement learning environments, or provide you the infrastructure for self-service evals.
Scope the work
We map the workflows and scenarios that matter for your business. You define the objectives.
Build expert evals
Vetted practitioners write the tasks, golden outputs, and pass/fail rubrics. Then we run your agent against those standards.
Get your scorecard
Find out where your agent fails and why, plus get the full eval suite. Re-run for every new model release to see how it performs.
FAQs
An eval is a rigorous, repeatable test of what your agent can actually do on your workload. It's a structured suite — tasks, rubrics, and scoring — that tells you whether your agent works, which model and configuration to run, and what each option costs.
Both customer-facing and internal agents. You get the same eval expertise the biggest frontier labs rely on, applied to your product — with proven coverage across finance, support, commerce, code, and operations for enterprise and application-layer teams.
Yes. You see the full price–performance frontier, so you can choose the model that clears your quality bar for the least cost, right-size reasoning and context to stop overpaying, and pinpoint which tools and context genuinely improve results.
Yes. Post-training ROI is a core use case — you'll know whether fine-tuning your own model actually pays off before you invest in it.
You're working with the same team and methodology behind the evals run with major AI labs — backed by the largest vetted expert network in the AI economy. That's 5M+ experts, 200k+ trained eval authors, 300+ professional domains and the rubrics and tooling refined at the frontier.
Three ways, from fully managed to fully self-serve: expert staffing (you direct the work while vetted experts are sourced and embedded for your team), managed delivery (you get validated evals delivered end to end, priced per task rather than per head), and self-serve (your team builds and runs evals on Mercor's platform and methodology).
Less than six weeks from first conversation to a reusable eval suite built for your team.
Yes — you keep a reusable suite built for your team, not a one-off report.
Your next release deserves a real bar
From first conversation to a reusable eval suite you own, in four to six weeks.