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
How do you guarantee quality forai assisted labelling?
Every project runs through multi-tier QA: annotators are benchmarked against gold-standard tasks before production, batches are statistically sampled against agreed accuracy targets, and ambiguous cases are escalated and documented in a living labeling guide. You receive accuracy reports with every delivery.
Can we start with a small pilot before committing?
Yes — we recommend it. A paid pilot batch on your real data lets you evaluate our quality, turnaround, and communication before scaling. Pilot learnings become the project's labeling guide.
How is our data kept secure?
Client data is encrypted in transit and at rest, access is limited to the assigned project team under NDAs, and we support VPN-restricted or client-hosted workflows where data cannot leave your environment. Retention and certified deletion terms are set per engagement.
What tools and output formats do you support?
We work in your annotation platform or ours, and deliver in the format your pipeline expects — COCO, YOLO, Pascal VOC, JSON, CSV, or a custom schema — with delivery via API, cloud bucket, or scheduled export.
Ready to scale yourai assisted labelling?
Start with a pilot batch — see our quality on your data before you commit.