[ STABLEBROWSE — FRONTIER DATA ]
We help frontier labs
build better models.
Hard-to-find real-world data — sourced, labeled, and enriched until it's worth training on. The corpora everyone has are exhausted. We supply the data nobody else can get.
[ 01 / THESIS ]
Every model is bounded by the data it never saw.
The easy corpora are exhausted, and synthetic data collapses inward. What remains is the hard part: real-world data that is long-tail, unstructured, gated, or simply never written down — and worthless until someone finds it, labels it, and makes it legible.
That is what we do. We find it, we label it, we make it legible.
[ 02 / CAPABILITIES ]
From the wild to the training run, in three steps.
01
Sourcing
The data your scrapers can't reach — long-tail, gated, ephemeral, offline. If it exists in the world, we can bring it into the run.
02
Labeling
Expert annotation where it counts. Domain specialists and model-assisted pipelines, cross-checked until the labels are worth training on.
03
Enrichment
Raw capture becomes training-grade: structured, deduplicated, verified, and traced back to its source. Every record arrives with provenance.
[ 03 / THE STANDARD ]
Every record earns
its place.
Nothing ships on volume alone. Each record is labeled by people who know the domain, checked for agreement, and delivered with provenance — so your team can trust it without re-auditing it.
[ 04 / TRUST ]
Why labs trust us with data.
Before StableBrowse, co-founder Jay Mehta co-authored MDD, an ICCV 2025 paper introducing a benchmark multimodal dataset for text-controlled and music-conditioned 3D duet dance generation.
MDD comprises 620 minutes of high-quality motion-capture data performed by professional dancers, synchronized with music, and detailed with over 10K fine-grained natural-language descriptions. The annotations capture spatial relationships, body movement, and rhythm, making MDD the first dataset to integrate human motion, music, and text for duet dance generation.
The paper introduced two benchmark tasks, Text-to-Duet and Text-to-Dance Accompaniment, and received the Outstanding Paper Award at the Interactive Human-Centric Foundation Models Workshop at ICCV 2025.
[ 05 / WHAT WE SUPPLY ]
Enterprise workflows
Internal enterprise data and process traces: tickets, documents, approvals, and tool use, captured from real organizations, permissioned and anonymized.
Egocentric data
First person capture across every dimension: RGB video, gaze, audio, depth, IMU motion, and hand pose, synchronized and annotated for embodied AI.
Long horizon agent tasks
Agent trajectories embedded in production workflows: episodes that span days, with real tools, real stakes, and measured outcomes.
Real world game data
Gameplay tasks and trajectories from real games: goals, actions, failures, and recoveries, annotated in fine detail.
Expert labels
Human judgment from people who actually know the domain, measured for agreement.
Custom collection
A capability gap on your side becomes a dataset spec on ours.
[ 06 / CONTACT ]
Tell us what
you can't find.
If it exists in the world, we'll get it into your next run.