Remediate – Let SMEs patch AI without engineers.

Apply expert answers instantly to improve AI agents in production, then pinpoint the data sources causing failures and correct them permanently.

Book a demo
Diagram of Cleanlab’s AI data quality platform

Improve and monitor your AI agent’s health.

Quickly close gaps and ship more reliable GenAI experiences.

An example dash board showing a table with columns for "Primary Issue", "Score", "Status", "Question", and "Answer". The "Primary Issue" column shows types such as "Hallucination" or "Unhelpful".

Apply Expert Answers

Leverage SME input to deliver immediate fixes in production. This blocks bad outputs from reaching users and improves safety.

Surface Critical Errors

Automatically group and prioritize high-impact, recurring failures for review based on trust scores and user impact.

Track All User Queries

Capture every prompt and instantly see how many responses are flagged or corrected.

Verify Prompt Adherence

Auto-generate evals to automatically check if each response meets your system prompt requirements.

SMEs make Generative AI more reliable.

Even the best models, retrieval systems, and data sources have gaps—leading to imperfect AI-generated responses. That’s why generative AI applications require your SMEs to validate some of the outputs, ensuring accuracy, reliability, and trust at scale.

A bar chart with the title “SME Responses on Production AI Agents”, showing two vertical bars. The y-axis is labeled “Accuracy”. The first bar is labeled “Without SMEs” and has a value of 72%. The second bar is labeled “With SMEs (and Cleanlab)” and has a value of 90%. Small text below the chart reads “Performance benchmarks above show aggregated data from multiple production AI agents (finance, support) with and without SME responses using Cleanlab.”

Engage SMEs with a seamless workflow.

Cleanlab aggregates unresolved questions, ranks them by priority, and enables SMEs to provide answers, filling knowledge gaps efficiently.

A visual workflow showing user questions flowing into SME review. Top shows 8 user questions, with examples like 'What is your return policy?' and 'Can I return my order?' A notification prompts SME to review questions. At the bottom, an SME provides a resolved answer to the question: 'What is your policy on returns after the 30 day return window?' Answer states exceptions may be made for manufacturing defects.

Apply Expert Answers

Leverage SME input to deliver immediate fixes in production. This blocks bad outputs from reaching users and improves safety.

Surface Critical Errors

Automatically group and prioritize high-impact, recurring failures for review based on trust scores and user impact.

Track All User Queries

Capture every prompt and instantly see how many responses are flagged or corrected.

Verify Prompt Adherence

Auto-generate evals to automatically check if each response meets your system prompt requirements.

Resolve failures fast without engineering.

A line graph with the title “Correct Responses with Cleanlab”. The x-axis shows a timeline of about a month. The line is steadily increasing. At the end of the line is a label saying “92% increase.”

Immediate updates without engineering.

Immediately applies SME-provided answers without requiring retraining, fine-tuning, or access to data.

Future proofs against similar questions.

Intercepts future similar questions using SME answers, continuously increasing accuracy while minimizing SME effort.

Separate data from model and retrieval issues.

Pinpoint whether AI failures stem from the LLM, retrieval, or data—so you can fix content issues without involving engineers.

Easy to integrate.

Getting started with Cleanlab is simple—just a few lines of code. Our Enterprise API connectors also let you work directly within your preferred applications (e.g., Slack).

Explore the docsBook a demo