Benchmarks-as-a-Service
We build independent, repeatable benchmarks for your agent system, run inside a faithful replica of the world it will meet, so you know exactly where it succeeds and where it fails.
Providers
Mock SaaS providers your organization can access.
| Name | Slug | Kind | |
|---|---|---|---|
| Salesforce | salesforce | API | |
| ServiceNow | servicenow | API | |
| Slack | slack | API | |
| Microsoft Teams | microsoft-teams | API | |
| Jira Service Management | jira-service-management | API | |
| Zendesk | zendesk | API | |
| HubSpot | hubspot | API | |
| Google Workspace | google-workspace | API | |
| GitHub | github | API | |
| Workday | workday | API | |
| NetSuite | netsuite | API | |
| Stripe | stripe | API | |
| Datadog | datadog | API | |
| PagerDuty | pagerduty | API |
Why this exists
Agents fail in production, often spectacularly. Usually it is because they were never tested anywhere close to the real thing — real data, real tools, real mess.
So we assemble those environments on demand, for regression tests, day-to-day evals, or a straight comparison between models before you commit to one. Each is grounded in something real: your task set and reference solutions, or your metadata.
The point is not a leaderboard score. It is honest evidence of where your system holds up and where it breaks, so you can keep hillclimbing.
How it works
- Clone and State
Replicate your software with its data, APIs, and failure modes, so agents are measured on your stack, not a toy
- Seed and Extrapolate
Bring a seed set of tasks with reference solutions, and we extrapolate it into thousands you never had to write
- Sandbox and Scale
Isolated sandboxes, forked and parallelized, so hour-long agentic evals finish faster and reproduce exactly
- Beyond the Leaderboard
Not just which model wins, but which one fits your work, and what it costs you in money and time
Use cases
- Before you ship
Regression tests and dev-time checks, so a prompt tweak or a model swap cannot quietly break what already worked.
- When you pick a model
Head-to-head comparisons on your own tasks, scored against cost and latency instead of a public leaderboard.
- After you ship
Day-to-day evals, customer-facing benchmark reports, and post-training signal drawn from the failures that matter.
Looking to benchmark your AI system? Book a call with us.
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