TrainAgentAI runs the data pipelines, the annotation workforce, and the feedback loops that turn raw signal into the training sets foundation models, robotics, and autonomous systems actually learn from.
Text, voice, image, and sensor data streams in directly from your systems.
Trained specialists across 180+ locales label, rank, and review every item.
A second reviewer plus automated checks catch disagreement before it ships.
Clean, schema-matched datasets land in your pipeline on schedule.
Trusted by teams building the next generation of AI
Every dataset moves through the same four stages — sourced, labeled, validated, delivered — but the workforce, tooling, and QA bar shift depending on what you're training.
Capture multimodal data — text, voice, image, video, sensor and robotics streams — from a vetted, geographically distributed network.
Trained annotators apply task-specific schemas, from bounding boxes to conversational rubrics, inside purpose-built tooling.
Multi-pass review, statistical sampling, and model-assisted QA catch drift before a dataset ever reaches your training run.
Structured exports, API delivery, or direct integration into your training infrastructure — versioned and auditable.
Sourcing pipelines that recruit, screen, and route contributors to the right multimodal task — image capture, voice recording, sensor logging — at the volume your training run needs.
RLHF pipelines pairing trained evaluators with structured rubrics to rank, critique, and rewrite model outputs at scale.
Dedicated workforce pods, SLAs, and compliance controls built around your security posture, not a generic crowd queue.
End-to-end orchestration from raw capture through delivery, with checkpoints your ML team can audit at every stage.
Teleoperation logs, trajectory labeling, and physical-world safety review tuned for embodied AI and autonomous systems.
Native speakers across 180+ locales handle translation, transcription, and culturally-aware annotation at scale.
A real timeline, because the order matters: each stage gates the next.
We map your model's failure modes to a labeling schema and pick the right contributor pool.
A small batch runs end-to-end so you can sanity-check quality before we scale workforce.
Workforce ramps to target throughput; QA sampling runs continuously, not just at the end.
Final dataset ships with a QA report, schema docs, and a direct line to the team that built it.
Text, voice, image, video, and sensor capture from a global contributor network.
Bounding boxes, segmentation, transcription, entity tagging, conversational labeling.
Multi-reviewer consensus, gold-set sampling, and statistical QA before delivery.
Dataset curation, benchmark construction, and eval-set design for your training runs.
Preference ranking, critique-and-revise, red-teaming, and reward-model data.
Teleoperation logs, trajectory labeling, and physical-world safety review.
Recruiting, training, scheduling, and quality management for your dedicated pod.
Custom eval suites, model comparison harnesses, and human-rated benchmark design.
Verified specialists in law, medicine, finance, and code review high-stakes model outputs.
Internal copilots and workflow agents grounded in your domain.
Trajectory labeling and physical-safety review for manipulation tasks.
Edge-case scenario sourcing and frame-level perception labeling.
Pretraining curation, RLHF, and frontier eval design.
Segmentation, detection, and classification at pixel-level precision.
Multilingual transcription, accent coverage, and speech-quality rating.
A robotics lab used our trajectory-labeling pipeline to retrain a grasping policy across 40,000 annotated demonstrations.
Read the case study →A foundation model team needed preference data outside English. We stood up native-speaker evaluator pods in three weeks.
Read the case study →An autonomous vehicle company needed rare-weather driving scenarios. Our network sourced and labeled 12,000 qualifying clips.
Read the case study →"We replaced three vendors with one TrainAgentAI pod. Turnaround on RLHF batches dropped from ten days to four."
Head of Data, Helios Labs
"Their QA sampling caught labeling drift our internal team had missed for two months. That alone paid for the engagement."
ML Lead, Atlas Robotics
"Onboarding a dedicated workforce pod took eight days, not eight weeks. The compliance docs were ready before we asked."
VP Engineering, Cascade ML