Cleanlab – Data-Quality & AI-Reliability Layer for ML and LLMs | Quasa.io
#Quasa #QUA #Cleanlab
Cleanlab is a data-quality and AI-reliability layer. It started as MIT research on confident learning: find bad labels without a second human pass. Cleanlab Studio is the no-code and Python tool that flags label errors, outliers, and near-duplicates in text, image, and tabular sets, then exports a reviewed “cleanset.” Detect, built on the Trustworthy Language Model, scores live LLM and agent answers from 0 to 1 so a weak reply can be blocked, rewritten, or sent to a person. Remediate is the human loop for fixing answers and the knowledge base. It deploys as SaaS or in a private cloud. Handshake acquired the company in January 2026; the standalone product path after that deal is not fully public.
𝐂𝗢𝗥𝗘 𝗦𝗧𝗥𝗘𝗡𝗚𝗧𝗛𝗦
• Finds label errors the model is quietly learning
• Works on text, images, and tables, no-code or API
• Trust score on live answers, not only on training data
• Guardrails for hallucinations, bad retrieval, and policy misses
• Human review only on the rows that actually look wrong
𝗜𝗗𝗘𝗔𝗟 𝗙𝗢𝗥
ML and support teams who already have a dataset or a bot and need fewer bad labels and fewer confident wrong answers.
𝗛𝗜𝗚𝗛𝗟𝗜𝗚𝗛𝗧𝗦
• Studio workflow: upload, flag, fix, export a cleanset
• Auto-label for high-confidence unlabeled rows
• Detect plugs into RAG, OpenAI-style APIs, LangChain, and agents
• Customers cited include banks and research groups
• Open-source roots still sit under the commercial product
𝗣𝗢𝗧𝗘𝗡𝗧𝗜𝗔𝗟 𝗖𝗢𝗡𝗦𝗜𝗗𝗘𝗥𝗔𝗧𝗜𝗢𝗡𝗦
• Handshake acquisition in 2026; confirm the product still ships as a standalone SKU
• Public pricing is thin; some writeups start near $100 a month, enterprise is quoted
• A trust score is a filter, not a proof the answer is true
• Studio still needs a person on the hard rows
• Not a labeling marketplace by itself
𝗢𝗩𝗘𝗥𝗔𝗟𝗟 𝗩𝗘𝗥𝗗𝗜𝗖𝗧
4.2/5 stars. Use Cleanlab to clean the set and to catch bad live answers. Do not treat the score as a guarantee. Earn 1 QUA reward by reviewing on Quasa.io too!
𝗚𝗘𝗧 𝗦𝗧𝗔𝗥𝗧𝗘𝗗: https://quasa.io/projects/cleanlab























