High-Volume Object Detection
Verifying large volumes of model-generated bounding boxes at a fraction of the cost of labeling from scratch.
Advanced & 3D Labeling
AI Assisted Labelling
Model-in-the-loop pre-labeling that speeds up throughput while trained experts verify every prediction.
Overview
The most efficient annotation pipelines don't treat humans and models as alternatives — they sequence them together. Vidyut Data runs workflows where a model (yours or ours) generates initial label predictions, and trained annotators review, correct, and resolve the cases the model gets wrong. This lifts throughput substantially while keeping final accuracy at human-verified standards, and every correction made by our team becomes usable signal for improving the next model iteration.
What's included
- ✓Pre-labeling using your existing models or ours
- ✓Human verification queues prioritized by prediction confidence
- ✓Iterative refinement cycles that focus review effort where it matters most
- ✓Correction tracking that highlights recurring model error patterns
- ✓Reporting on throughput and cost compared to fully manual annotation
- ✓Compatible with your existing annotation platform or ours
Use cases
New Model Bootstrapping
Generating an initial labeled dataset quickly, then refining it through successive review cycles as the model improves.
Production Model Monitoring
Reviewing live model outputs on an ongoing basis to catch drift and feed corrections back into retraining data.
Legacy Dataset Refresh
Re-reviewing and enriching older annotation sets using current model assistance to bring them up to modern labeling standards.
Frequently asked questions
Do we need to bring our own model, or do you provide one?
Either works. We can pre-label using your model or a suitable one on our side — the human verification layer is the same regardless of whose model generates the first pass.
How do you make sure the model's errors don't slip through?
Human reviewers verify predictions rather than rubber-stamping them, with lower-confidence outputs prioritized for closer review. Automation speeds the work; it doesn't replace the check.
Can this work inside our existing annotation platform?
Yes — the workflow is tool-agnostic and can run in your platform or ours.
Can we start with a small pilot before committing?
Yes. A pilot is the clearest way to see the throughput gain versus fully manual labeling on your actual data.
Ready to scale your ai assisted labelling?
Start with a pilot batch — see the quality of the data before you commit.