Six capabilities. One platform. Designed from the ground up for engineering teams who ship AI to production.
Each capability integrates with the others. Run them individually or chain them into a complete evaluation pipeline.
Use a secondary LLM to score your primary model's outputs against predefined criteria. Objective, fast, and calibrated to your standards.
Run adversarial test cases to probe model safety boundaries, jailbreak resistance, and edge-case behavior before users find them first.
Every model change runs against your golden test set. Regressions are caught at the PR level, not in the incident report.
Track how your evaluation datasets evolve. Reproduce past evaluations exactly. Know which test cases were active when that regression slipped through.
One webhook. Full evaluation pipeline triggered on every PR. Results post to GitHub, GitLab, or Slack — wherever your team works.
Run the same test suite across GPT-4o, Claude, Llama, and your fine-tuned variants. Pick the model that actually performs best on your workload.
A straightforward four-step flow from model connection to deployment gate.
Add your API endpoint via our SDK or config file. Supports OpenAI-compatible interfaces, Anthropic, Mistral, Cohere, and self-hosted endpoints.
Write evaluation cases in Python or YAML. Define expected behaviors, factual constraints, and scoring thresholds for each capability you're testing.
Evaluations run in parallel across all test cases. Results are scored, tracked over time, and available in your dashboard within seconds.
Set pass/fail thresholds. Failing evaluations block your CI pipeline. Passing ones give you the green light to merge and deploy.
The LLM Grader replaced three days of manual review per sprint. It's not perfect, but it's calibrated well enough that we trust it to block bad merges.
Red teaming caught a jailbreak vector two weeks before our enterprise launch. That one find alone justified the subscription cost for the year.
Multi-model comparison is the feature I didn't know I needed. We switched providers and saved 40% on inference costs after running a proper benchmark.
Drop Confident AI into your current workflow without changing how your team builds.
Native Actions integration. PR checks, status updates, and evaluation reports — all in your existing GitHub workflow.
GitLab CI/CD integration with merge request blocking, pipeline stages, and evaluation artifact storage.
Evaluation results and alerts posted to Slack channels. Your team sees failures the moment they happen, not after the deploy.
A clean, well-documented Python library. Write your evaluation logic as code, version it with your model, and test it like any other module.
Full REST API for any language, any pipeline, any tool. If it can make an HTTP request, it can use Confident AI.
Run the evaluation engine on your own infrastructure. Enterprise plans include on-premises deployment with full data residency control.
We'll connect to your model, run a live evaluation, and show you what the CI integration looks like end to end.
Confident AI