Services
Image, video, text, 3D, robotics, and agent evaluation work delivered by managed expert pods with clear QA, review, and case-study-backed operating patterns.
Masks, boxes, classes, and edge-case review
Pixel-accurate image labeling programs for detection, classification, instance segmentation, and semantic segmentation. Built for frontier vision teams that need consistent labels, review trails, and calibrated QA.
Temporal labels at frame precision
Frame-level boxes, segmentation, event labels, action recognition, and multi-view tracking. We manage reviewer calibration and sampling so long-form video work stays consistent across batches.
Preference data with audit trails
Human preference ranking, rubric-based scoring, hallucination review, red-team evaluation, and SFT data validation. Every decision can be traced back to a rubric, reviewer, and quality checkpoint.
Industrial data with specialist review
Annotation and quality review for CAD drawings, manufacturing specs, process documents, and 3D production context. Designed for workflows where domain knowledge matters as much as labeling speed.
Robot training data scoped to the task
Motion capture, egocentric video, hand pose, scene context, manipulation traces, and robotics task data. We scope hardware, workflow, and exports around your robot body and training objective.
Ground-truth tasks for tool-using agents
Multi-step terminal tasks, validation harnesses, oracle traces, and model failure analysis. We build benchmarks that expose real gaps in agent planning, execution, and recovery.
Operating model
We turn model goals into task taxonomies, quality bars, domain requirements, and acceptance criteria.
Tbrain manages expert sourcing, onboarding, reviewer calibration, throughput, and issue escalation.
AI-native checks, human QA, sampling, and audit trails keep output quality visible before delivery.
Final exports include labels, metadata, review notes, and delivery context your training team can act on.
Tell us the task type, domain, quality bar, and delivery format. We will map the expert pod, QA model, and first milestone.
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