Cleanlab helps teams build safer AI agents by preventing incorrect responses from reaching users. Detect and remediate incorrect responses from any AI
Users praise Cleanlab for its effectiveness in identifying and resolving errors in datasets, enhancing data quality, and fostering reliable model training, particularly within the realm of Data-Centric AI. However, there are no specific user complaints highlighted in the available mentions. The tool is appreciated within the open-source community, evidenced by its significant following and adoption. Users seem satisfied with the value provided by Cleanlab, as it has not raised any specific concerns on pricing, maintaining a positive overall reputation.
Mentions (30d)
0
Reviews
0
Platforms
2
GitHub Stars
11,390
884 forks
Users praise Cleanlab for its effectiveness in identifying and resolving errors in datasets, enhancing data quality, and fostering reliable model training, particularly within the realm of Data-Centric AI. However, there are no specific user complaints highlighted in the available mentions. The tool is appreciated within the open-source community, evidenced by its significant following and adoption. Users seem satisfied with the value provided by Cleanlab, as it has not raised any specific concerns on pricing, maintaining a positive overall reputation.
Features
Use Cases
Industry
information technology & services
Employees
36
Funding Stage
Merger / Acquisition
Total Funding
$30.0M
459
GitHub followers
43
GitHub repos
11,390
GitHub stars
1
npm packages
2
HuggingFace models
🚀 How to enhance the accuracy of AI agents in customer support TLM (Trustworthy Language Model) accurately scores generative AI responses, boosting trust and reliability in AI-driven support. 🎥 Fu
🚀 How to enhance the accuracy of AI agents in customer support TLM (Trustworthy Language Model) accurately scores generative AI responses, boosting trust and reliability in AI-driven support. 🎥 Full demo: 🔗 https://t.co/nmwbRiCD33 Ways TLM aids customer support teams: 🧵👇
View original🚀 New from Cleanlab: Expert Guidance AI agents running multi-step workflows can fail in tiny, trust-breaking ways. Expert Guidance lets teams fix these behaviors with simple human feedback, instant
🚀 New from Cleanlab: Expert Guidance AI agents running multi-step workflows can fail in tiny, trust-breaking ways. Expert Guidance lets teams fix these behaviors with simple human feedback, instantly. ✈️In one airline workflow: 76% → 90% after only 13 guidance entries. https://t.co/nv7e1zZUa0
View originalCleanlab is now integrated into @langfuse's observability platform! We're adding real-time trust scores to LLM outputs to quickly surface the most problematic responses for Langfuse users. https://t.
Cleanlab is now integrated into @langfuse's observability platform! We're adding real-time trust scores to LLM outputs to quickly surface the most problematic responses for Langfuse users. https://t.co/nYT1Av0iRx
View original2/ Extract customer information & order details for accuracy 🔍 With batch processing, TLM extracts relevant data at scale, enabling faster, more efficient AI-powered support.
2/ Extract customer information & order details for accuracy 🔍 With batch processing, TLM extracts relevant data at scale, enabling faster, more efficient AI-powered support.
View original🚀 How to enhance the accuracy of AI agents in customer support TLM (Trustworthy Language Model) accurately scores generative AI responses, boosting trust and reliability in AI-driven support. 🎥 Fu
🚀 How to enhance the accuracy of AI agents in customer support TLM (Trustworthy Language Model) accurately scores generative AI responses, boosting trust and reliability in AI-driven support. 🎥 Full demo: 🔗 https://t.co/nmwbRiCD33 Ways TLM aids customer support teams: 🧵👇
View originalStill think LLMs can’t reason? That’s getting harder to believe with today’s new o3-mini model. But even with o3-mini, these models can definitely still hallucinate… Automatically score the trustwo
Still think LLMs can’t reason? That’s getting harder to believe with today’s new o3-mini model. But even with o3-mini, these models can definitely still hallucinate… Automatically score the trustworthiness of responses from any model with TLM, now including o3-mini! https://t.co/aukLzW2joG
View originalWorried your AI agents may hallucinate incorrect answers? Now you can use Guardrails with trustworthiness scoring to mitigate this risk. Our newest video shows you how, showcasing a Customer Support
Worried your AI agents may hallucinate incorrect answers? Now you can use Guardrails with trustworthiness scoring to mitigate this risk. Our newest video shows you how, showcasing a Customer Support application that requires strict policy adherence. https://t.co/viCuaUtS0h
View originalIntroducing Agentic RAG with LLM trustworthiness estimates -- A framework to ensure reliable answers in Retrieval-Augmented Generation and keep latency/costs in check. The idea: Assess response trust
Introducing Agentic RAG with LLM trustworthiness estimates -- A framework to ensure reliable answers in Retrieval-Augmented Generation and keep latency/costs in check. The idea: Assess response trustworthiness and then adjust retrieval plans to ensure sufficient context [...🧵] https://t.co/UwVXy8FH9J
View originalHow many "r" in strawberry?? Today we're excited to announce a new way to catch and explain hallucinations from any LLM! It’s been over a year since the release of GPT-4, but these models remain f
How many "r" in strawberry?? Today we're excited to announce a new way to catch and explain hallucinations from any LLM! It’s been over a year since the release of GPT-4, but these models remain fundamentally unreliable and risky to use in high-stakes applications. The https://t.co/FtVi8vXmL1
View originalProduct Announcement: Introducing Cleanlab Studio Auto-Labeling Agent! Annotating a dataset? Save time with Auto-Labeling Agent, which suggests new labels with confidence levels - completing your da
Product Announcement: Introducing Cleanlab Studio Auto-Labeling Agent! Annotating a dataset? Save time with Auto-Labeling Agent, which suggests new labels with confidence levels - completing your dataset effortlessly. https://t.co/JUT1rVNxWv
View originalAnnouncing the Trustworthy Language Model, a solution to the biggest problems in productionizing GenAI: hallucinations and reliability. TLM provides a reliable trustworthiness score for every LLM out
Announcing the Trustworthy Language Model, a solution to the biggest problems in productionizing GenAI: hallucinations and reliability. TLM provides a reliable trustworthiness score for every LLM output and can also produce more accurate outputs than GPT-4. https://t.co/ZjuPXaxrbH
View originalOpen-Source AI aficionados: you've probably heard of the new Open-Source AI Cookbook from @huggingface At the top of this amazing resource, you'll now find a new notebook: Detecting Issues in a Tex
Open-Source AI aficionados: you've probably heard of the new Open-Source AI Cookbook from @huggingface At the top of this amazing resource, you'll now find a new notebook: Detecting Issues in a Text Dataset with Cleanlab 👇 https://t.co/Hr8UW9D05v [...]
View originalBad data costs the U.S. $3 Trillion per year. Your company's structured data has errors due to data entry or measurement mistakes, sensor noise, pipeline bugs, etc. Announcing 📣 an AI solution to
Bad data costs the U.S. $3 Trillion per year. Your company's structured data has errors due to data entry or measurement mistakes, sensor noise, pipeline bugs, etc. Announcing 📣 an AI solution to catch erroneous values in *any* tabular dataset: https://t.co/ihhjuWdRxK
View originalBIG news for open-source practitioners of Data-Centric AI: We just released major updates to cleanlab, the most popular software library for Data-Centric AI (with 8000 GitHub stars thanks to an amazi
BIG news for open-source practitioners of Data-Centric AI: We just released major updates to cleanlab, the most popular software library for Data-Centric AI (with 8000 GitHub stars thanks to an amazing community) Check out the repo and read on ... https://t.co/N6h6NqgZW9
View original📢 Announcing a multimodal AI for: E-Commerce & Retail businesses Big product catalogs develop many issues: miscategorized products, near duplicates, low-quality images/descriptions, unsafe conte
📢 Announcing a multimodal AI for: E-Commerce & Retail businesses Big product catalogs develop many issues: miscategorized products, near duplicates, low-quality images/descriptions, unsafe content, … These are hard-to-catch and harm customer experience + revenue, but [...🧵] https://t.co/EUPVL9jhxe
View original🚀 Exciting news for #LLM enthusiasts! We've unveiled the fastest method to curate clean training data for LLMs during fine-tuning for Q/A tasks. Say goodbye to the hurdles that prevent your LLM fro
🚀 Exciting news for #LLM enthusiasts! We've unveiled the fastest method to curate clean training data for LLMs during fine-tuning for Q/A tasks. Say goodbye to the hurdles that prevent your LLM from moving from demo to production. More details 👇 https://t.co/p7U8VNWIKB
View originalRepository Audit Available
Deep analysis of cleanlab/cleanlab — architecture, costs, security, dependencies & more
Cleanlab uses a tiered pricing model. Visit their website for current pricing details.
Key features include: Automated data quality checks, Anomaly detection in datasets, Data cleaning and preprocessing tools, Model performance monitoring, Bias detection and mitigation, Compliance tracking and reporting, User-friendly dashboard for insights, Integration with popular data platforms.
Cleanlab is commonly used for: Ensuring data integrity for machine learning models, Identifying and correcting data entry errors, Monitoring data pipelines for anomalies, Evaluating model predictions against ground truth, Enhancing compliance with data regulations, Improving trust in AI outputs for stakeholders.
Cleanlab integrates with: Python, R, Pandas, Apache Spark, AWS S3, Google Cloud Storage, Microsoft Azure, Tableau, Power BI, Snowflake.
Cleanlab has a public GitHub repository with 11,390 stars.
Based on user reviews and social mentions, the most common pain points are: breaking.
Based on 55 social mentions analyzed, 0% of sentiment is positive, 100% neutral, and 0% negative.