Open-source foundation
DeepEval, the open-source framework behind Confident AI, lets engineers write evaluations as tests alongside their code.
SERVICES / EVALUATION PLATFORMS
Application-specific metrics with Confident AI.
Independent planning and implementation for teams considering or using Confident AI and the open-source DeepEval framework: metrics that match your workflow and regression tests that run with every change.
Where Confident AI fits
A broad metric library
Ready-made metrics cover areas such as answer relevancy, faithfulness, tool correctness and task completion, plus custom rubric metrics like G-Eval.
Shared results
The Confident AI platform adds shared datasets, result history and review for teams beyond engineering.
Choosing the right metrics
Start from the workflow
We pick metrics that answer your questions, rather than enabling every metric available and drowning in numbers.
Custom criteria where needed
Generic metrics rarely capture your policies. We write custom rubric metrics and code checks for business rules.
Thresholds that mean something
Every metric gets a calibrated threshold based on expert-reviewed examples, not a default value.
How we help
Build regression suites
Versioned test cases that run in CI on relevant changes, with critical checks blocking releases.
Evaluate agent behavior
Tool-call and task-completion checks for agents, combined with outcome verification in your own systems.
Calibrate the judgment
Compare metric scores with domain reviewers, document disagreements and keep a held-out set for honest comparison.
Related services
Using DeepEval on its own?
If your team runs the open-source DeepEval framework without the platform, see our DeepEval services for agent metrics and CI regression suites.
Explore serviceEvaluation Assessment
A baseline for one Confident AI agent or workflow, with prioritized findings and a release recommendation.
Explore serviceHallucination Testing
Measure how often your Confident AI agent invents facts, policies or actions, and which fixes reduce it.
Explore serviceFrom scope to handover
Start with your inputs
Representative tasks, expected outcomes, repository and platform access, and a domain reviewer.
Keep reusable assets
Test files, custom metrics, thresholds, a baseline report, prioritized findings and a documented rerun process.
Support after handover
We can retest fixes, extend coverage to new Confident AI workflows or move to recurring Managed Evaluations as your agent changes. Platform usage and licenses stay in your account.
Make your next decision with evidence.
Start with one workflow, clear success criteria, and an evaluation plan your team can use.