LLM Evals For The Entire Team. Not Just Engineers.

Postman for AI evaluation. Connect via API, simulate conversations, and test entire AI workflows — not just prompts. No CSVs. No waiting on engineering.

Evals ran to date[-21,916,950,212+]

HOW IT WORKS

Experiment without the engineering bottleneck.

1. 01

   Connect any AI app in minutes.

   Point to any endpoint like Postman. Send requests, tweak prompts, see results live.

2. 02

   Define your golden dataset and metrics.

   Upload test cases, pick the metrics that matter, set thresholds for "good".

3. 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.

4. 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.

POST https://api.your-app.com/v1/chat

Params Headers Body Auth JSON

{
    "model": "gpt-4o",
    "messages": [
        {
            "role": "user",
            "content": "How do I dispute a charge?"
        }
    ]
}

Response 200 OK 412 ms 1.2 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.

Metrics 4 / 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.

Ship Only When Evals Pass

Required eval checks on every PR. Quality regressions block the merge.

PLATFORM

Testing you'll actually want to run.

Simulated user
I need to return a jacket
AI agent
I can help with that. Do you have the order #?
Simulated user
Order #8847-AX.
AI agent
Found it. Navy jacket from May 5.
Simulated user
Yes, that's the one.
AI agent
Return initiated. Label sent to your inbox.

SIMULATION RESULTS

• 100% 96% 99% 91% 94% 86% pass rate
• Passed 2,064
• Failed 336
• Avg turns 5.2
• P50 latency 1.2s
• Coherence 0.91
• Hallucinations 4.2%

Multi-turn conversation testing

Simulate full conversations end-to-end and catch failures that only surface across multiple exchanges.

VARIANT DIFF

• prompt v1.2→v1.3 temp 0.7→0.4

Side-by-side experiments

Change any variable — model, prompt, system logic — and compare results across every metric and pipeline step.

METRIC ALIGNMENT

• n=1,240 3 evaluators aligned
• CONFUSION MATRIX vs. human label

HUMAN PASS

• 842 true pos
• 56 false pos
• EVAL FAIL
• 38 false neg
• 304 true neg
• PRECISION 0.94
• RECALL 0.96
• F1 0.95
• κ 0.84

OVERALL 92%

Alignment metrics with humans

Compare metric scores against human annotations to surface false positives and negatives.

• evals.yml on: push, pull_request PASS 94%

TRIGGER
COMMIT
DURATION
EVAL SCORE

• prompt edit main#a3c91e1 m 04s → 0.91 +0.03

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.

METRICS

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.

More on LLM evaluation

• LLM evaluation metrics guide
• Why LLM-as-a-judge works
• What makes a good eval
• Compare LLM evaluation tools
• How Humach shipped voice AI 200% faster