AI writes the data. People make it right.
Human review and correction of AI-generated training data — captions, scene descriptions, and agent decisions — checked against the source video and sensor data. We catch hallucinations, safety-critical omissions, and contradictions before they reach your model.
Machine-made data needs a human check.
More and more AI is trained on data that other AI models generated. It is fast and cheap — but generative models hallucinate. They describe a car that isn’t there, miss a pedestrian stepping into the road, read a tail light as a brake light, or copy a speed value wrongly. If those errors reach your training set, your model learns them.
Our trained, English-proficient reviewers watch the source footage, read the reference data, and correct the AI’s output only where it is wrong. You get a dataset that is free of errors — without paying for rewrites of text that was already correct.
Scene captions
AI descriptions of camera footage — road layout, weather, traffic signals, brake lights and turn signals, pedestrians, vehicles cutting in — checked against the video.
Situation & driving context
Summaries that combine video with vehicle telemetry (speed, braking, steering), surrounding-object lists and driver circumstances — with close attention to wrongly transcribed numbers.
AI agent decisions (JSON)
Structured outputs showing whether an assistant should act, how urgent it is, the reasoning behind it, and which actions or voice alerts it chose — reviewed field by field for sound judgement.
Other AI-generated labels
The same method applies to any machine-written caption, label, summary or structured output that has to match a source of truth — images, video, documents or sensor logs.
Four principles every reviewer follows.
We calibrate to your specification, then apply it the same way across every reviewer and every sample — so a correction on sample one means the same thing as a correction on sample four thousand.
Errors first, not polish
We fix hallucinations, omissions and contradictions. Text that is already correct stays as it is — no costly rewrites for style.
Safety-weighted review
Missed pedestrians, signals, brake lights and cut-ins are top-priority defects. We also avoid overstating danger where there is none.
Consistent across the chain
When a caption changes, we check that the situation summary and the agent’s decision still agree with it.
Flag it, don’t guess it
We never assert what can’t be seen. Unclear or conflicting samples are recorded and reported to your team, not forced.
Staged delivery, checked twice.
Work runs in stages that match your pipeline — for example, descriptions are corrected and delivered first, your system regenerates the agent outputs from them, and we then review those. One reviewer corrects, a second reviewer checks, and calibration sessions keep judgements aligned across the team. We work in your annotation tool or ours, and every engagement starts with a small paid pilot before full volume.
Teams whose AI has to see clearly.
Autonomous Driving
Verified scene descriptions and situation summaries for self-driving training and evaluation datasets.
Driver Assistance (ADAS)
Human-checked data on signals, lamps, pedestrians and cut-ins — the events that matter most for safety.
In-Car AI Assistants
Review of agent reasoning, urgency scores, tool choices and voice alerts to the driver.
Vision-Language Models
Hallucination checks on image and video captions produced by VLMs before they become training data.
Agentic AI
Field-by-field review of structured agent outputs: is the decision reasonable, and do the actions match it?
Robotics & Mobility
Correction of machine-generated descriptions from cameras and sensors on robots, drones and fleets.
Start with a small pilot.
Send us your specification and a sample batch. We’ll agree the quality bar, throughput and price per sample — then prove it on real data before you commit to scale.