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Measure everything your AI agent tells customers

Stop relying on manual vibe checks. Scorable replaces guesswork with automated AI-driven judges that monitor behavior in production and prevent harmful content before customers see them.

From the community

Measure with confidence

How to ensure your AI agents are delivering quality results?

Get visibility into the black box of AI agents and chatbots — so you can build better products.

What teams run into

Vibe checks are biased and slow.

You rely on experts to review outputs by hand, which doesn’t scale.

Debugging agents stopped being fun.

You’re stuck chasing regressions instead of shipping improvements.

You shouldn’t need to be a data scientist.

You want clear signals without building a full analytics stack.

Outcomes that compound over time

  • Get visibility and insights on the behavior of your AI agent.
  • Customize the automated evaluations in minutes for quick wins.
  • Align automatic evaluations with your business KPIs over time.
  • Improve your agents to deliver quality outputs, prevent hallucinations, and maximize accuracy.

What changes when you measure

Iterate quickly on your agent KPIs to match your business needs. Leverage evaluations to optimize LLMs, judges, and prompts for the best balance of quality, cost, and latency.

Steps to launch

Measure in three simple steps

Continue to improve your AI-powered products in production.

Step 1

Build AI judges in minutes, customized to your customer interactions.

The rich evaluation signals for compliance, hallucination detection, relevance - and custom agent failure modes.

Step 2

Embed the judges into your code to monitor AI in production.

Evaluate AI performance in real time, immediately identify issues that impact product quality.

Step 3

Detect and correct subtle errors in agent interactions.

Reduce 90% of manual work - Only alert the human expert when necessary.


Don't just log outputs. Judge them.

Our specialized Judges sit between your AI and your user, scoring every interaction against your specific policies.

USER INPUT

"Summarize the Q3 report."

LLM RAW OUTPUT

"Revenue grew by 20% due to the new product launch."

SCORABLE LOGIC LAYER

"judge_verdict": {
  "score": 0.2,
  "justification": "Statement not found in source text. Source says revenue was flat."
}
Docs

Python

JavaScript/TypeScript:

How It Works

  1. Your application sends requests to our proxy URL instead of OpenAI's
  2. Your tailored judge improves the response automatically based on it's feedback

Start by creating a judge by describing what you want to measure


Know what to fix, instantly.

Scorable analyzes your evaluation results and surfaces actionable insights — delivered to your dashboard or Slack.

INSIGHTS 12/12/2025 — 19/12/2025

Wins
  • •Overall quality improved vs. the previous period: average score increased ~18.9% to 0.777.
  • •Clear high performers: "Email Response Judge" (avg ≈ 0.858), "Product Recommendations Judge" (avg ≈ 0.826).
  • •Release v1.2 showing consistent quality improvements across all judges.
Issues
  • •"Returns Policy Judge" (avg ≈ 0.496) — likely impacting customer experience in refund flows.
  • •"Appointment Scheduling Judge" (avg ≈ 0.651) (staging environment) with high volume — needs attention before scaling.

Enterprise-Grade Sovereignty

SOC 2 Type IIGDPR CompliantDeploy AnywhereModel Agnostic