About Us
Vidyut Data provides expert-led data annotation, labeling, and curation for teams building Computer Vision, NLP, and enterprise data systems — the groundwork that determines whether a model holds up in the real world.
A model can only be as good as what it learns from, and in practice most AI failures trace back to the data, not the architecture. A brilliant model trained on inconsistent labels will fail in production, quietly and expensively. Our mission is to remove that failure point. We pair trained specialists with quality processes rigorous enough that the data we hand back is ready to train on as-is — no silent errors waiting to surface later. Whether you're a small team labeling your first batch or an enterprise running a long-term data operation, we treat the accuracy of your model as the real measure of our work.
Every batch moves through layered review — independent checks, benchmarking against reference tasks, and sampling audits — before it ever reaches you. We'd rather catch a problem in our own QA than have your model find it in production.
Our annotators are trained, managed people who get to know your domain and stay on your project — not click-workers cycling through tasks they've never seen before. Consistency comes from familiarity, and familiarity comes from keeping the same team on your data.
Sensitive and regulated data is handled with access controls, activity tracking, and processes built around your compliance needs from the start — not bolted on after the fact.
We don't hand over a dataset and disappear. As your model reveals where it's still weak, we fold those failure cases back into the guidelines and the next round of labeling — so the data improves in step with the model.
Good annotation isn't just about the labeling — it's about how the whole engagement is run. Here's what working with us actually looks like, from first contact to ongoing delivery.
We start small on purpose. Before you commit to anything large, we take a representative slice of your data, build the labeling guide with you rather than guessing at it, and deliver a paid pilot batch. The first thing you judge us on is real output — not a sales pitch.
Once the guide is agreed, we assign a dedicated team to your project and train them against it, measuring their work on reference tasks until it consistently holds at your accuracy bar. The same people stay on your data as it scales, so context compounds instead of resetting with every new batch.
Production work is never single-pass. Every batch runs through layered review — independent checks, sampling audits, and a defined escalation route for the edge cases that don't fit the guide cleanly — before anything is marked ready for delivery.
You receive data in your own format, alongside a plain-read quality summary. And the work doesn't freeze there: as your model reveals where it's still weak, those failure patterns feed straight back into how we label the next round — so the data keeps improving in step with the model.
Tell us about your project — we'll respond within one business day.
