Independent product intelligence · Worldwide · 2026-05-01 → 2026-07-31
Detect and remediate hallucinations in any LLM application.
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Product profile
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“Cleanlab”
Detect and remediate hallucinations in any LLM application.
4 of 8 signal groups are available for this product: identity, authority and backlinks, AI referrals, search demand.
Not measured for this domain: traffic and engagement, audience demographics, country distribution, acquisition mix. Those sections are left out of the page rather than filled with estimates.
Canonical website: https://help.cleanlab.ai/tlm
Observed across 2 discovery sources.
Research questions
Every answer is tied to a source we collected, and the questions with no evidence behind them are left unanswered rather than guessed.
It solves the problem of hallucinations and incorrect answers from Large Language Models that can undermine business reliability. Cleanlab's Trustworthy Language Model scores the trustworthiness of responses from any LLM in real-time to identify which outputs are correct and which need scrutiny.
TLM is used to add reliability to any LLM application, including RAG, Agents, Chatbots, Summarization, Data Extraction, Structured Outputs, Tool Calls, Classification, Data Labeling, and LLM Evaluations. It can be used to score existing model responses or as a replacement to produce higher accuracy outputs.
It is for developers building reliable AI applications who need to detect hallucinations in any LLM. The product is positioned as developer infrastructure and is compatible with enterprise applications.
Key features include state-of-the-art trustworthiness scores for any LLM application, the ability to improve LLM responses for higher accuracy, and a scalable real-time API. It works out of the box without needing training on user data and offers flexible latency/cost configurations.
Benchmarks show TLM can reduce incorrect responses from GPT-4o by 27%, o1 by 20%, and Claude 3.5 Sonnet by 20%. In RAG applications, it detects incorrect answers with 3x greater precision than other hallucination detectors and real-time Evaluation models.
TLM does not need to be trained on your data, so no dataset preparation or labeling work is required. It offers private deployment options for enterprise use.
The product is categorized under 'Developer & AI Platform' alongside 2,618 other products. The measured median domain authority for products in this category is 22.
It is an AI API from Cleanlab that scores the trustworthiness of responses from any Large Language Model in real-time to detect hallucinations and unreliable outputs.
Behind the lock
Worldwide estimates · Data period 2026-05-01 → 2026-07-31. Figures are measured estimates intended for market research. Signals we have not measured for this domain are omitted, never estimated; a measured zero is still reported as zero. How this was measured