data-centric-ai
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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.
The Startup Teaching Cameras to Catch What Tired Human Eyes Miss on the Assembly Line
Akridata spent years fixing AI's boring, expensive data problem - then pointed the same tools at factory defects and got acquired by DIMAAG in 2026.
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.
Corvic AI
Corvic AI is a Mountain View startup building an Intelligence Composition Platform - the logic layer that turns messy, multi-structured enterprise data (PDFs, tables, sensor logs, images, time series) into reliable, production-ready AI outcomes without bespoke pipeline engineering. Founded in 2023 by engineers from Intel, Katana Graph, and Determined AI, it uses proprietary techniques like Mixture of Spaces embeddings and agentic, auditable orchestration to deliver explainable enterprise intelligence for manufacturing, industrial, financial services, and life sciences customers.
Snorkel AI
Snorkel AI is a Redwood City-based enterprise AI company spun out of the Stanford AI Lab in 2019. Its Data Development Platform lets enterprises programmatically label, curate, and evaluate the training and evaluation data that powers custom LLMs, RAG systems, and AI agents - turning expensive manual annotation into a software-engineering discipline.