The evaluation platform for models and agents that have to work.
Turn "it seemed fine in the demo" into a number you can defend. Versioned eval suites, graders calibrated against experts, and statistical gates that run on every merge.
Everything between a prompt change and a confident release.
Versioned eval suites
Datasets, graders and thresholds are versioned together, so a score from March means the same thing in October. Branch, diff and review suites like code.
1,400 calibrated graders
Deterministic checks, model-based judges and rubric graders for faithfulness, policy adherence, tool use, safety and tone. Each one ships with its measured agreement with expert humans.
Trajectory evaluation
Score every step of an agent run: tool selection, arguments, side effects and final environment state. Simulated users drive realistic multi-turn scenarios.
Expert review in the loop
Route low-confidence cases to your reviewers or to our vetted specialists. Disagreements become calibration data for the next grader version.
Gates that respect noise
Confidence intervals, multi-seed runs and power analysis, so you block real regressions and stop chasing random variation.
Online evals on live traffic
Sample production traces, score them with the same graders, and alert when quality drifts. Any failure becomes a new test case with one click.
Start from 1,400 graders. Write the ones only you need.
Graders cover the categories that matter in production, and every one documents how it fails. Custom graders take a rubric, a policy file or a few lines of Python.
| Category | Graders | Human agreement |
|---|---|---|
| Groundedness & citation faithfulness | 86 | κ 0.84 |
| Tool-call policy & argument safety | 142 | κ 0.91 |
| Prompt injection resistance | 118 | κ 0.88 |
| Harmful compliance (bio, cyber, self-harm, …) | 203 | κ 0.86 |
| Over-refusal & helpfulness | 74 | κ 0.79 |
| PII & confidential data egress | 97 | κ 0.93 |
| Domain policy (finance, health, legal) | 311 | κ 0.81 |
| Task success, tone, format, cost | 369 | κ 0.82 |
Four steps to a release gate.
Threat-model the use case
We map what your system can do (read, write, pay, send) to the failure modes that would hurt, and turn that into eval categories with owners and thresholds.
Assemble the suite
Combine library graders, your historical incidents, synthetic cases generated by attacker agents and a gold set labelled by domain experts.
Baseline and calibrate
Run against your current production system, check grader agreement on a labelled sample, and set gates that reflect real risk tolerance.
Wire it into CI and production
Every pull request, prompt edit or model swap runs the suite. Live traffic is sampled and scored with the same graders.
Your data stays where your policies say.
- Managed
- Multi-region SaaS in the US and EU. Fastest start.
- Single-tenant
- Dedicated infrastructure and encryption keys per customer.
- Your VPC
- Helm and Terraform deployments on AWS, Google Cloud and Azure. Nothing leaves your account.
- On-prem / air-gapped
- For regulated and public-sector environments, including open-weight graders that run without external API calls.
- Integrations
- GitHub, GitLab, Jenkins, Buildkite, OpenTelemetry, Databricks, Snowflake, BigQuery, Datadog, Slack, Jira.
Evals FAQ.
What is an LLM evaluation platform?
An LLM evaluation platform runs a model, RAG pipeline or agent against a versioned set of test cases, scores every output with graders (code checks, model-based judges or humans), and tracks those scores over time so you can compare versions and catch regressions before release. See our complete guide to LLM evaluation.
How do you evaluate AI agents, not just single responses?
Reinforced Evals scores the full trajectory: every tool call, its arguments, intermediate reasoning artefacts and the final state of the environment. Graders can assert on actions ("no refund above $500 without approval") as well as on text, and simulated users drive multi-turn scenarios.
Can we trust model-based graders?
Only when they've been checked. Every grader in our library ships with its measured agreement against expert human labels, and you can calibrate custom graders against your own labelled sample. When agreement drops below threshold, cases are routed to human review automatically.
How many test cases do we need?
Enough to detect the regression you care about. For a pass rate around 95%, detecting a 2-point drop with 95% confidence needs roughly 1,700–2,200 cases per category, fewer with paired comparisons on the same cases. The platform computes required sample sizes and flags categories that are under-powered.
Does it work with our existing tools?
Yes. Evals ingests OpenTelemetry traces, runs from any CI system through the CLI, exports results to your warehouse, and integrates with GitHub, GitLab, Jira, Slack, Datadog and Databricks.
What does it cost?
There is a free developer tier. Team plans start at $1,900 per month; Enterprise is priced on evaluated volume and deployment model. See pricing.
Related: LLM evaluation guide · Evals vs. guardrails vs. red teaming · Reinforced Guard
Find out what your model does before your users do.
A 45-minute briefing with our evaluation team. Bring a use case; leave with a threat model, a draft eval plan and a sense of where you stand.