Data-Hub started where most annotation problems start: good models held back by inconsistent labels. We built a delivery model that puts a single accountable lead in front of every project and measures quality before it ever reaches your pipeline.
Annotation is often treated as a volume game — push frames through the cheapest queue and hope quality averages out. For modern computer vision, geospatial and language systems, that math doesn't hold. The cost of a wrong label compounds downstream into retraining, missed detections and lost trust in the model.
So we run differently. Every engagement is owned by a Vienna-based project lead who agrees the taxonomy, communicates in your timezone and signs off on acceptance. A dedicated, managed delivery team handles throughput at consistent quality. The result is a single standard from the first pilot batch to a pipeline running at scale.
We work with AI labs and applied teams across Europe, the US and South Korea — in mobility and infrastructure, earth observation, and language & LLM projects — backed by domain experts — doctors, biologists, engineers, financial experts, software developers and more — covering 10+ languages in-house. We keep each engagement small enough to stay accountable and large enough to scale when the model is ready.
A Vienna-based lead owns scope, taxonomy, communication and acceptance — your single point of contact.
A managed, domain-briefed team handles annotation throughput at a consistent standard.
Dedicated human review on every batch, with inter-annotator agreement tracked.
Signed confidentiality and access controls scoped to your project, built for EU expectations.
Headquartered in Vienna, we operate across Europe, the Americas and Asia-Pacific — delivering high-quality annotation under EU data standards with timezone-aware coordination.
We track agreement, IoU and acceptance per batch and share them. "High quality" only means something with numbers attached.
One lead owns your project end to end. You never chase an anonymous queue for an answer on scope or status.
We'd rather earn the pipeline with a pilot batch than win it on a pitch. Small first, scale on results.
Tell us about the model you're building and the data behind it. We'll suggest the fastest path to a clean, training-ready dataset.