data-annotation
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Girl Power Talk Bets the Client Brief on the Person With the Shortest Résumé
The Mohali social enterprise sells serious digital work to global clients, then hands meaningful responsibility to young people whom conventional employers might still be asking to fetch coffee.
Taskeater Outsourced the Clickwork - Then Outsourced Its Own Name
Taskeater made a business of the unglamorous work hiding behind fast-growing internet companies. Its bigger trick was recognizing when cheap hands were no longer a complete strategy.
CapeStart's Real Product Is the Last Mile of Enterprise AI
Most AI projects do not die in the demo. They stall in the unglamorous stretch between a clever model and a system that regulated teams can trust. CapeStart has built a 600-plus-person business around crossing that gap.
The Company Betting That AI Still Needs a Human in the Loop
Label Studio put data labeling in the hands of a million AI practitioners. Now HumanSignal is wagering that the harder generative-AI models get, the more they need human judgment behind them.
CNTXT AI Is Building the Arabic Layer the Global AI Boom Skipped
The Abu Dhabi company is betting that the next enterprise AI advantage will not come from a bigger model. It will come from cleaner local data, Arabic that sounds like people actually speak, and systems that never have to leave the region.
Fusion CX Is Turning the Call Center Into an AI Test Kitchen
The global outsourcer built its reach one delivery center and acquisition at a time. Now it is using millions of real customer interactions to turn contact-center software into an operating advantage.
micro1 Bet the AI Boom Would Run Out of Human Judgment
The young company began with an AI interviewer. It is now selling the scarce ingredient frontier models, workplace agents and robots all need: structured evidence of how capable people think and work.
Mundo AI
Mundo AI (YC W25) builds high-quality multilingual training data for AI models, tackling the shortage of non-English data that leaves most of the world's languages underserved by today's models. The company partners with native speakers and runs end-to-end operations in the countries where target languages are spoken, using proprietary software for data collection, generation, annotation, and quality assurance. Founded in 2024 by four University of British Columbia alumni, its customers are AI labs and companies training or improving non-English and multimodal models.
Kili Technology
Kili Technology is a Paris-based enterprise software company that helps large organizations build high-quality datasets to train, fine-tune, and evaluate AI models. Founded in 2018 by Edouard d'Archimbaud and François-Xavier Leduc, its platform spans image, video, text, PDF, and geospatial annotation, and has increasingly focused on LLM evaluation, RLHF, and expert-driven data curation for generative AI. Backed by a $25M Series A led by Balderton Capital, Kili works with enterprises including SAP, Airbus, Louis Vuitton, Michelin, and Crédit Agricole.
iMerit Technology
iMerit is a global AI data solutions company that supplies the labeled, enriched and expert-refined data used to train, fine-tune and evaluate machine learning and generative AI models. Founded in 2012 by Radha Basu, it pairs a specialized human workforce with its Ango Hub annotation platform and its Scholars network of domain experts to serve Fortune 500 clients across autonomous vehicles, healthcare, geospatial and financial services. In June 2026, enterprise-AI firm EXL agreed to acquire iMerit for up to $310 million.
Stardust AI
Stardust AI is a San Francisco-founded, data-centric AI company that helps enterprises turn raw, messy, multimodal data into the high-quality datasets that machine-learning models actually need. Through its Rosetta auto-labeling platform, its MorningStar data engine and its COSMO dataset offerings, Stardust annotates and manages 2D/3D/4D data spanning images, video, point clouds, audio and text - with heavy expertise in autonomous driving and large language models. The company works with names like SAIC Motor, BYD, Geely, Bosch, ZF, Xiaomi and JD.com, and pitches a simple thesis for the AI era: whoever controls the data controls the model.
Superb AI
Superb AI is a computer vision AI company that builds an end-to-end MLOps and DataOps platform, letting teams label, curate, train, and deploy vision models on images, video, and 3D LiDAR data. Founded in 2018 and a Y Combinator alum, it uses AI-assisted automation to shrink the slow, manual work of preparing training data, and serves enterprise customers across autonomous driving, manufacturing, security, and public sector from offices in San Mateo and Seoul.
Deepen AI
Deepen AI is a Silicon Valley company building the data lifecycle infrastructure for autonomous systems and physical AI. Its platform combines multi-sensor data annotation, sensor calibration (LiDAR, camera, radar, IMU), and validation tools that carmakers, robotics firms, and AV developers use to turn raw sensor data into reliable training data. Founded in 2017, Deepen also co-created Safety Pool, a global scenario-sharing initiative launched with the World Economic Forum and WMG at the University of Warwick.
Pareto.AI
Pareto.AI is a San Francisco-based, talent-first human data platform that connects AI labs with a deeply vetted network of expert labelers to produce premium training data, RLHF, and evaluation signals. Founded by Thiel Fellow Phoebe Yao, the company started as a bootcamp for women and work-from-home mothers, pivoted into AI data labeling in 2023, and now positions itself as the 'verification layer' for reinforcement learning on real-world expertise, turning specialist judgment into durable reward signals under the banner 'Expert data for AGI.'
Thoth AI
Thoth AI is a Singapore-headquartered global AI data solutions company that builds the high-quality datasets, evaluation frameworks, and human-in-the-loop operations that production AI systems depend on. Spanning 200+ languages and 100+ countries, it handles data collection and annotation across images, video, 3D point cloud, text, and audio, along with RLHF, model evaluation, content moderation, and multilingual customer experience for teams building LLMs, VLMs, multimodal systems, and applied AI like robotics.
Wow AI
Wow AI is a US-registered, end-to-end AI training-data company that supplies high-quality, multilingual datasets and human-in-the-loop services - data collection, transcription, annotation, validation and RLHF - to teams building large language models, voice assistants and computer-vision systems. Its crowdsourcing network spans 170,000+ contributors across 120+ languages, with 100,000+ hours of audio and domain datasets for finance, healthcare and retail. The founding team has since extended the vision into AIxBlock, a decentralized platform for building, training and deploying AI, and WowDAO, a community-owned AI ecosystem.
Deccan AI
Deccan AI is a Mountain View-based GenAI data company that runs the post-training and production layer for frontier labs and enterprises. It builds super-accurate SFT and RLHF datasets, reinforcement learning environments and agentic evaluation pipelines using an elite expert network and a purpose-built quality platform.
Sama
Sama is a San Francisco-based AI training-data company that combines a 5,000-plus full-time annotation workforce - most based in Kenya, Uganda and India - with proprietary tooling to deliver labeled data, model evaluation and red-teaming for the world's largest AI builders. Founded as the nonprofit Samasource in 2008 by the late Leila Janah, it relaunched as a for-profit B Corp in 2019 and counts Google, Walmart, NVIDIA, Ford and Microsoft among its customers.
Surge AI
Surge AI is a San Francisco data annotation company that produces high-quality human-labeled datasets and RLHF feedback for the world's leading AI labs - OpenAI, Google, Anthropic, Meta and Microsoft among them. Founded in 2020 by ex-Twitter and ex-Facebook ML engineer Edwin Chen, it bootstrapped to roughly $1.2B in annual revenue with around 110 employees and a labeler network reported near one million.