The data beneath better AI
SEAGEM collects, labels and evaluates training data with a vetted global crowd and expert reviewers, so your models learn from examples people have checked.
What we deliver
From raw collection to final evaluation, each stage can run on its own or as one pipeline.
Data collection
Speech, text, image and video gathered to your spec, with consent and provenance recorded for every sample.
Annotation and labeling
Bounding boxes, segmentation, transcription, entity tagging and more, reviewed in two passes before delivery.
RLHF and alignment
Preference ranking, rubric scoring and expert-written demonstrations that teach models how to respond.
Model evaluation
Side-by-side comparisons, red-teaming and safety reviews that show where a model fails before users find out.
Agent and coding data
Multi-step task trajectories, tool-use traces and reviewed code solutions written by vetted engineers.
Physical AI data
Sensor fusion, LiDAR point clouds and robot demonstration labeling for autonomy and robotics teams.
Quality you can audit
Every batch ships with its review history: who labeled it, who checked it, and how often they agreed.
We qualify contributors with task-specific tests, then measure them against gold-standard samples throughout the project. Work that drifts below the agreed threshold is pulled and redone before you see it.
How a project runs
Scope
We agree on data specs, guidelines, quality metrics and timeline.
Pilot
A small batch tests the guidelines. You review it and we adjust.
Scale
The trained crowd ramps up, with daily quality reports.
Deliver
Data arrives in your format, with full review history attached.
Secure by default
Sensitive projects run in access-controlled workspaces. Contributors sign NDAs, devices can be restricted, and data never leaves the environment you approve.
We support data residency requirements and delete project data on a schedule you set.
Tell us what your model needs to learn
We reply within one business day with a scoped pilot proposal.