Confident AI

Define organization-wide eval standards

Translate your standards into controls that can be checked — operational, runtime, and pre-deployment. This ensures that the criteria for being 'ready to ship' is objective and not left to opinion.

Create policies for different AI use cases

Group controls into a policy tailored to your organization, ensuring that each environment, such as Staging and Production, has its own set of standards to meet before deployment.

Enforce it automatically, every day

Controls are re-evaluated across every project on a scheduled basis, making compliance a continuous process rather than a last-minute scramble before reviews.

See who's compliant, and who's accountable

A comprehensive report covers every project, its compliance status, and the responsible owner. This transparency helps identify gaps early and assigns accountability.

Connect any AI app in minutes

Easily integrate any AI application by pointing to its endpoint, allowing for immediate testing without the need for SDK changes or extensive setup.

Define your golden dataset and metrics

Upload test cases and select the metrics that are most relevant to your evaluation. Set thresholds to determine what constitutes a 'good' performance.

Run experiments on your whole app

Conduct evaluations on the entire application rather than just isolated prompts. This allows for the detection of multi-turn failures that may not be evident in single prompt tests.

Ship confidently, without the bottleneck

Integrate evaluations into your CI/CD pipeline, ensuring that required checks are passed before any code merges, thus preventing regressions.

Simulate full conversations end-to-end

Test your AI applications by simulating complete conversations, allowing you to catch failures that may only surface across multiple exchanges.

Automated evals on every change

Automatically run evaluations on every code change, similar to GitHub actions, allowing product managers and domain experts to tweak prompts and see results without engineering bottlenecks.

Instrument with two lines of code

Quickly set up observability by integrating the SDK or using OpenTelemetry, allowing for full tracing of your AI applications in just minutes.

Evaluate every trace automatically

Run evaluation metrics on 100% of traces instead of sampling, providing a complete view of changes across versions and ensuring quality.

Know the moment quality drops

Set thresholds for any metric and receive immediate notifications if quality drops, allowing teams to address issues before they impact users.

Let your next eval dataset builds itself

Automatically curate datasets from production traces, filtering and tagging them for easy regression testing against future changes.

Connect your AI app in minutes

Quickly integrate red teaming capabilities by pointing to any endpoint, allowing for immediate security assessments without extensive setup.

Pick the security framework that fits

Choose from established frameworks like OWASP LLM Top 10 or NIST AI RMF, or create custom policies to assess vulnerabilities relevant to your application.

Get a clear risk assessment

Replay thousands of adversarial probes against your application and score each finding by CVSS, providing detailed insights into vulnerabilities and remediation guidance.

See where risk is concentrating across your portfolio

Continuously run red teams across all AI applications, allowing you to monitor risk trends and focus on areas that require immediate attention.