Mission
AI is moving beyond chatbots into video, audio, images, and interactive environments - models that need to understand how the world looks, sounds, moves, responds, and changes over time. Progress on that frontier depends on one thing more than any other: data with enough precision, diversity, and quality to actually teach a model what good looks like. That is where we operate.
What we do
First, we source and aggregate video, audio, image, and interaction data across real-world, digital, and simulated environments. Then we filter for semantics, rights, artifacts, and task quality; index billions of videos, images, and audio clips with purpose-built detectors and embeddings; annotate at scale with dense labels, before-and-after editing pairs, temporal alignment, transcripts, action metadata, camera signals, UI events, and custom schemas backed by human QA; and deliver training-ready datasets, evaluation sets, and environments through secure, encrypted transfer.
The pipeline
We source and aggregate video, audio, image, and interaction data across real-world, digital, and simulated environments.
We filter for semantics, rights, artifacts, and task quality.
We index billions of videos, images, and audio clips with purpose-built detectors and embeddings.
We annotate at scale - dense labels, before-and-after editing pairs, temporal alignment, transcripts, action metadata, camera signals, UI events, and custom schemas, backed by human QA.
And we deliver training-ready datasets, evaluation sets, and environments through secure, encrypted transfer.
Compliance runs through all five stages, not just the last one - filtering, licensing, consent, and retention are built into how we source and process data, not bolted on afterward.
Behind every dataset is a network of trained specialists, calibrated reviewers, and quality leaders operating within rigorous human-in-the-loop frameworks. Every task follows defined standards, measurable quality controls, and documented audit trails.
We work directly with AI research and product teams to improve model performance through custom annotation, evaluation, and SFT programs. From schema design to bespoke data collection, we build workflows around the exact capabilities a model needs to learn.
At scale, precision matters. Our platforms and contributor network support millions of interactions across text, image, video, and audio, ensuring consistent, reliable datasets from pilot to production.
Why it matters
That is the whole idea behind what we do. Not just labeled data, but data built with the precision, scale, and judgment that training frontier models actually requires.
If you are building AI that needs to understand and interact with the real world - not just perform on a benchmark - we would like to hear what you are working on.