# cleanlab.ai

> AI-optimized mirror of cleanlab.ai containing 50 pages totalling 34,449 words of clean markdown content, structured data, and semantic HTML. Original source: https://cleanlab.ai/. Last updated: 2026-05-17T20:00:07.884Z.

Each page is available as HTML (with JSON-LD structured data) and Markdown (text-only, ideal for LLMs and RAG).

## Homepage

- [Keep AI mistakes away from your customers.](/site-root.html): Cleanlab helps teams build safer AI agents by preventing incorrect responses from reaching users. Detect and remediate incorrect responses from any AI agent to ensure safety, compliance, and trust at scale. (548 words)

## Articles & Blog Posts

- [blog/learn/cleanlab-2/index.html](/blog/learn/cleanlab-2/index.html) (1 words)
- [blog/learn/active-learning-transformers/index.html](/blog/learn/active-learning-transformers/index.html) (1 words)
- [blog/tlm-o1/index.html](/blog/tlm-o1/index.html) (1 words)
- [blog/learn/active-learning/index.html](/blog/learn/active-learning/index.html) (1 words)
- [blog/tlm-structured-outputs-benchmark/index.html](/blog/tlm-structured-outputs-benchmark/index.html) (1 words)
- [blog/safeguarding_personal_data_with_tlm/index.html](/blog/safeguarding_personal_data_with_tlm/index.html) (1 words)
- [blog/reliable-agentic-rag/index.html](/blog/reliable-agentic-rag/index.html) (1 words)
- [blog/rag-tlm-hallucination-benchmarking/index.html](/blog/rag-tlm-hallucination-benchmarking/index.html) (1 words)
- [Popular Real-Time Evaluation Models](/blog/rag-evaluation-models/index.html): A comprehensive benchmark of evaluation models to automatically catch incorrect responses across five RAG applications. (2,256 words)
- [blog/llm-accuracy/index.html](/blog/llm-accuracy/index.html) (1 words)
- [Real-Time Trust Scoring for Agents](/blog/tau-bench/index.html): Evaluating autonomous failure prevention for AI agents on the leading customer service AI benchmark. (1,781 words)
- [terms/index.html](/terms/index.html) (1 words)
- [news/index.html](/news/index.html) (1 words)
- [blog/data-centric-ai/index.html](/blog/data-centric-ai/index.html) (1 words)
- [privacy/index.html](/privacy/index.html) (1 words)
- [Existing Benchmarks for Structured Outputs](/blog/structured-output-benchmark/index.html): Existing Structured Outputs datasets are unreliable, so we created four new ones. (1,728 words)
- [What’s new in cleanlab 2.3:](/blog/learn/cleanlab-2-3.html): Highlighting what's new in cleanlab 2.3 (1,126 words)
- [research/index.html](/research/index.html) (1 words)
- [sales/index.html](/sales/index.html) (1 words)
- [The most important problem for the future of AI is reliability](/blog/learn/announcing-cleanlab-studio/index.html): Cleanlab Studio for Enterprise launches to automate data curation for LLMs and the modern AI stack with $5 million in seed funding from Bain Capital Ventures. (1,300 words)
- [Watch](/talks/index.html): Cleanlab helps teams build safer AI agents by preventing incorrect responses from reaching users. Detect and remediate incorrect responses from any AI agent to ensure safety, compliance, and trust at scale. (220 words)
- [Issues in ImageNet](/blog/learn/automated-data-quality-at-scale/index.html): A fully-automated analysis of errors in the ImageNet training set. (1,254 words)
- [Trustworthy Language Model](/blog/4o-claude/index.html): Benchmarking hallucination detection via the Trustworthy Language Model, with the newest models from OpenAI and Anthropic. (1,087 words)
- [Building a Reliable Customer Support Agent with LangGraph](/blog/prevent-hallucinated-responses/index.html): A case study on a reliable Customer Support Agent built with LangGraph and automated trustworthiness scoring (1,480 words)
- [About Cleanlab – We make AI safe to trust.](/about/index.html): Cleanlab helps organizations build AI they can trust. Our platform ensures every response is safe, accurate, and aligned with business goals. (557 words)
- [TL;DR: AI Agent Safety as Enterprise Infrastructure](/blog/ai-agent-safety/index.html): AI agents are moving into enterprise workflows, but unpredictability remains at every step. Leaders must understand four risk surfaces and how to contain them with layered safety systems. (1,695 words)
- [TL;DR](/blog/emerging-reliability-layer-agent-stack/index.html): AI agents succeed when teams separate the Core and Reliability stacks. The Core drives differentiation through architectures, prompts, tools, and context. Reliability ensures trust with guardrails, monitoring, and validations. Learn why the split matters and how top teams deliver agents that are both innovative and dependable. (1,444 words)
- [TL;DR](/blog/expert-guidance/index.html): Once your AI agents are live, the hard part begins: keeping them reliable. Cleanlab’s new Expert Guidance feature shows how non-engineers can teach AI systems to think and act better instantly, in natural language. (1,083 words)
- [The Challenge of LLM Evaluation: Balancing Quality and Efficiency](/blog/tlm-lite/index.html): TLM Lite allows you to generate high-quality responses using advanced LLMs while employing smaller models for fast and cost-effective trustworthiness scoring. (1,054 words)
- [TL;DR](/blog/managing-ai-apps-with-humans/index.html): From guardrails to remediation, people keep AI agents aligned in production. Discover the oversight roles, levels of involvement, and steps engineering leaders can take to scale responsibly. (1,437 words)
- [Learn more about Data-Centric AI](/blog/learn/index.html): Explore Cleanlab's blog for the latest research, tutorials, and insights into AI and data science. Stay informed about new features, company news, and best practices. (411 words)
- [Reduce Hallucinations with Trustworthiness Filtering](/blog/simpleqa/index.html): Benchmarking LLM trustworthiness scoring mechanisms to improve LLM abstention and response-generation. (962 words)
- [Addressing the biggest problem in analytics and AI: reliability](/blog/series-a-announcement/index.html): A personal perspective on the importance of clean data as Cleanlab announces $30M in funding to bring automated data curation to enterprise AI. (857 words)
- [Introducing Expert Answers](/blog/expert-answers/index.html): AI agents often give wrong, IDK, or unhelpful answers that frustrate users. Expert Answers let nontechnical SMEs instantly fix these cases, making your AI more helpful without waiting for engineers. (874 words)
- [The Problem with Annotation](/blog/learn/auto-labeling/index.html): Generate AI, not headaches. Automate annotation with AI. (866 words)
- [Letter from the CEO: Handshake acquires Cleanlab](/blog/handshake-acquires-cleanlab/index.html): Cleanlab has been acquired by Handshake AI. (719 words)
- [Letter from the CEO: Handshake acquires Cleanlab](/blog/index.html): Explore Cleanlab's blog for the latest research, tutorials, and insights into AI and data science. Stay informed about new features, company news, and best practices. (192 words)
- [Overcoming Obstacles in RAG Systems](/blog/announcing-document-curation/index.html): Generate AI, not headaches. Automate heterogenous data source curation with Cleanlab document support. (419 words)
- [tlm/index.html](/tlm/index.html) (1 words)
- [blog/learn/synthetic-image-with-stable-diffusion/index.html](/blog/learn/synthetic-image-with-stable-diffusion/index.html) (1 words)
- [Better Reporting and Decision Making](/blog/learn/studio-ecommerce/index.html): Using AI to analyze product listings for errors, and how this boosts the accuracy of product categorization and analytics efforts. (1,603 words)
- [TL;DR](/blog/inside-trustworthiness-guardrail/index.html): Even advanced AI models still hallucinate, producing confident but wrong answers that can harm trust and compliance. Cleanlab’s trustworthiness guardrails, powered by the Trustworthy Language Model (TLM), block inaccurate responses in real time and deliver safe fallback or expert-verified answers to keep AI systems reliable in production. (1,055 words)
- [Engineering Leaders Survey – AI Agents in Production 2025](/ai-agents-in-production-2025/index.html): Discover how engineering leaders running AI agents in production are building, scaling, and improving reliability. This Cleanlab research study reveals what works, where teams struggle, and the best practices shaping enterprise AI in 2025. (1,868 words)
- [Contact us](/contact/index.html): Cleanlab helps teams build safer AI agents by preventing incorrect responses from reaching users. Detect and remediate incorrect responses from any AI agent to ensure safety, compliance, and trust at scale. (9 words)
- [Security](/security/index.html): Cleanlab helps teams build safer AI agents by preventing incorrect responses from reaching users. Detect and remediate incorrect responses from any AI agent to ensure safety, compliance, and trust at scale. (45 words)
- [Trust Scoring Works on Any Agent](/blog/agent-tlm-hallucination-benchmarking/index.html): Using AgentLite to study how much LLM trust scoring can reduce incorrect responses from popular agentic frameworks: Act, ReAct (zero/few shot), PlanAct, PlanReAct. (1,294 words)
- [LLMs’ biggest challenge: hallucinations](/blog/trustworthy-language-model/index.html): TLM scores the trustworthiness of outputs from any LLM in real-time via state-of-the-art uncertainty estimation. (2,677 words)
- [Detect – Check every response generated by AI.](/detect/index.html): Check every AI response in real time with guardrails. Cleanlab detects hallucinations, missing context, and other issues by scoring each output for trust and accuracy. (381 words)
- [Careers at Cleanlab](/careers/index.html): Explore exciting career opportunities at Cleanlab, where you can contribute to cutting-edge AI technology. Discover open positions, company values, and the benefits of working at Cleanlab. (150 words)

## Resources

- [Full Page Index](/index.html): Browse all cached pages with rich metadata
- [About This Cache](/content/about.html): Methodology, technical details, and usage guidelines
- [XML Sitemap](/content/sitemap.xml): Machine-readable sitemap for crawler discovery
- [Robots.txt](/content/robots.txt): Crawler directives