Multi-turn conversation testing
Simulate full conversations end-to-end and catch failures that only surface across multiple exchanges. Test your app the way your users actually use it.
LLM Evals Your Team Will Love. Not Dread.
Postman for AI evaluation. Connect via API, simulate conversations, and test entire AI workflows — not just prompts. No CSVs. No waiting on engineering.
TRUSTED BY 500+ LEADING AI COMPANIES
Evals ran to date[ 0+ ]
HOW IT WORKS
Experiment without the engineering bottleneck.
- 01
Connect any AI app in minutes.
Point to any endpoint like Postman. Send requests, tweak prompts, see results live.
- 02
Define your golden dataset and metrics.
Upload test cases, pick the metrics that matter, set thresholds for "good".
- 03
Run experiments on your whole app, not just a prompt.
Run your dataset against your live app — not a playground. Catch multi-turn failures.
- 04
Ship confidently, without the bottleneck.
Wire evals into CI. Required checks block regressions before they merge.
Connect Any Endpoint
Point at any AI app like Postman. No SDK, no code changes.
POSThttps://api.your-app.com/v1/chat Send
ParamsHeadersBodyAuthJSON
{
"model": "gpt-4o",
"messages": [,
{
"role": "user",
"content": "How do I dispute a charge?"
}
]
}
Response 200 OK412 ms1.2 KB
{
"id": "chatcmpl-9f2a…",
"output": "To dispute a charge, open…",
"latency_ms": 412
}
Golden Dataset & Metrics
Upload test cases, pick the metrics that matter, and set thresholds.
InputExpected OutputTag
How do I cancel my subscription?Go to Settings → Billing and click Cancel…
billing
Why was my card declined?A decline usually happens for one of three…
payments
Can I get a refund for last month?Refunds are processed within 5–7 business…
refunds
What's covered under the basic plan?The basic plan includes core access to…
plans
Metrics4 / 50+
Answer Relevancy≥ 0.85
Faithfulness≥ 0.80
Hallucination≤ 0.10
Task Completion≥ 0.75
Tone≥ 0.70
Side-by-Side Experiments
Change a model, prompt, or pipeline step. See what improved and what regressed.
Baselinegpt-4o · prompt v3
Experimentgpt-4o · prompt v4
MetricBaselineExperimentΔ
Answer Relevancy
0.82
0.91
+0.09Faithfulness
0.74
0.88
+0.14Hallucination
0.18
0.06
-0.12Task Completion
0.69
0.84
+0.15Tone
0.81
0.79
-0.02Ship Only When Evals Pass
Required eval checks on every PR. Quality regressions block the merge.
Tune system prompt for billing flow#482feat/billing-prompt5 commits12 files
Some checks were not successful1 failing, 4 successful
ci / lintPassed in 14s
ci / unit-tests42 tests passed in 1m 12s
deepeval / answer-relevancy0.91 ≥ 0.85Required
deepeval / faithfulness0.88 ≥ 0.80Required
deepeval / hallucination0.21 > 0.10 thresholdRequired
Merging is blocked
The base branch requires all required eval checks to pass before merging.
Merge pull requestView failing eval →
PLATFORM
Testing you'll actually want to run.
Side-by-side experiments
Change any variable — model, prompt, system logic — and compare results across every metric and pipeline step. See exactly what improved and what regressed.
Alignment metrics with humans
Compare metric scores against human annotations to surface false positives and negatives. Know exactly where your evals agree with your team — and where they don't.
Automated evals on every change
Think GitHub actions for evals. Product managers and domain experts can tweak prompts, and evaluations will run automatically.
MCP-native workflow
Evaluate, iterate, and ship without leaving your favorite IDE — Cursor, Claude Code, or any MCP-compatible editor. Run evals, pull team results, and push fixes in one workflow.
METRICSMetrics your org can rally behind.
Metrics your org can rally behind.
Powered by DeepEval.
50+ research-backed eval metrics used by teams at OpenAI, Google, and Microsoft — from hallucination and faithfulness to tone, safety, and task completion.
INTEGRATIONS
Works with your stack. All of it.
Evaluate with any model provider, instrument with any framework, and run evals in any CI/CD pipeline.
Model Providers
OpenAI
Claude
Gemini
Azure OpenAI
AWS Bedrock
Vertex AI
Mistral
LiteLLM
PortkeyOpenAIClaudeGeminiAzure OpenAIAWS BedrockVertex AIMistralLiteLLMPortkeyOpenAIClaudeGeminiAzure OpenAIAWS BedrockVertex AIMistralLiteLLMPortkeyOpenAIClaudeGeminiAzure OpenAIAWS BedrockVertex AIMistralLiteLLMPortkey
Frameworks
LangChain
LlamaIndex
CrewAIOpenAI Agents
Vercel AI SDK
LangGraph
PydanticAI
OpenTelemetryLangChainLlamaIndexCrewAIOpenAI AgentsVercel AI SDKLangGraphPydanticAIOpenTelemetryLangChainLlamaIndexCrewAIOpenAI AgentsVercel AI SDKLangGraphPydanticAIOpenTelemetryLangChainLlamaIndexCrewAIOpenAI AgentsVercel AI SDKLangGraphPydanticAIOpenTelemetry
CI/CD
GitHub Actions
GitLab CI
Jenkins
CircleCI
Buildkite
Azure PipelinesGitHub ActionsGitLab CIJenkinsCircleCIBuildkiteAzure PipelinesGitHub ActionsGitLab CIJenkinsCircleCIBuildkiteAzure PipelinesGitHub ActionsGitLab CIJenkinsCircleCIBuildkiteAzure Pipelines
TESTIMONIALS
Trusted by companies that take AI seriously.
FinomBefore Confident AI, a single improvement cycle took 10 days — I'd create a task, assign it to an engineer, wait for availability, and go back and forth. Now the same cycle takes three hours, and our product managers can run it themselves.
Igor Kolodkin,Head of AI Quality, Finom
Confident AI saves us 480+ hours of manual AI evaluation every month — and gives us the data to defend every quality decision in front of engineering, product, and leadership.
Anoop Mahajan,Director of QA, Amdocs
Confident AI gave our team one place to turn production failures into datasets, align metrics, and keep regressions out of releases without waiting on custom engineering work.
SD
Senior Director of Engineering,Fortune 500 medical device company
HumachWe run a lot of large-scale, multi-turn simulations, and Confident AI made it far easier to design scenarios and execute those tests without piecing together external tools.
Sean Austin,Chief AI Officer, Humach
Thanks to Confident AI, we were able to move to a fine-tuned model and cut our LLM costs by 80%. This opens up whole new use cases now to generate better output with more targeted LLM calls.
John Lemmon,AI Lead, Supernormal
FAQ
Have a Question?
Checkout our FAQs below, or talk to a human. They won't hallucinate.
If your AI app is reachable through APIs, no. Point to any endpoint and start sending requests — just like Postman. No SDK, no code changes, no engineering dependency to start running evals.
We offer 50+ research-backed metrics mainly using LLM-as-a-judge evaluators that use a language model to assess quality, tone, safety, and more. Every metric is powered by DeepEval, the open-source evaluation framework used by teams at OpenAI, Google, and Microsoft.
Yes. Unlike most eval tools that only test single prompts, you can simulate full multi-turn conversations end-to-end and catch failures that only surface across multiple exchanges.
Change any variable — model, prompt, system logic — and run your golden dataset against both versions. Results are compared side by side across every metric and pipeline step so you can see exactly what improved and what regressed.
No. Engineers can connect endpoints and configure pipelines. Product managers and domain experts can tweak prompts, run experiments, and evaluate results — no engineering bottleneck required.
Yes. We support major LLM providers like OpenAI, Anthropic, and Google. Cloud providers like Bedrock, Vertext, and Azure OpenAI, and gateways such as Portkey and LiteLLM.
We offer a built in tool to help you know if your app is returning the correct content for testing. Payloads are flexiable and outputs can be parsed from any format you return.