A data company that shows its working
We build training data for AI teams: collected with consent, annotated by people who know the domain, measured before it ships. Based in Bihar, India, with a vetted contributor network across 150+ languages.
Started because the alternative was unusable
Karmyug began with a team that had spent years on the buying side of AI data, writing guidelines, receiving batches, and discovering in evaluation that a third of the labels were built on a misreading nobody had raised.
The fix was not better annotators. It was a pipeline that catches problems while they are cheap: a capture check before upload, an automated pass within thirty seconds, and gold tasks hidden in the stream so quality is measured continuously rather than sampled at the end.
That pipeline is now the company. The contributor network grew around it, and so did the willingness to tell a client when a quota is unrealistic before the contract is signed.
Mission
Make high-quality, consented training data available to any team building AI, not only to the ones who can afford an in-house data org.
Vision
A data supply chain where provenance, consent and quality are verifiable by default, and where the people producing the data are paid properly for it.
Four things we will not trade away
These cost us deals occasionally. They are still the right calls.
Say the number, or say you don't have it
Accuracy claims without an agreement score are marketing. Every delivery carries the statistics behind the figure, including the slices where raters disagreed.
Consent is part of the dataset
A file without a consent record is a liability, not an asset. Every item we deliver carries one, and a deletion job that honours it.
Recruit to the spec, not to the queue
An open crowd call returns urban young men. Quota-based recruitment costs more and takes longer, and it is the only way a diversity requirement means anything.
Pay the people doing the work
Transparent per-task rates, a published payout schedule, and an appeal route on every rejection. Contributors treated as disposable produce disposable data.
What the network gives you
Measured, not asserted
Every delivery ships with inter-annotator agreement, gold-set accuracy and a rejection breakdown.
Consent attached to every file
A consent record and a deletion job travel with the data, so it stays usable commercially.
Recruited to the spec
Demographic, dialect and device quotas, not whoever happened to be in the queue that week.
Checks before upload
Capture-time validation rejects unusable audio on the device, not three days later in review.
Pilot before scale
A small paid batch you accept first. It costs a week and it has saved every client who took it.
Honest about limits
If a language, quota or deadline is unrealistic, you hear it during scoping rather than in week six.
Work with us
Salaried and contract roles in Bihar, India and remote, plus linguists, annotators and reviewers across 150+ languages paid per task. Same quality bar either way.
Ready to build your next model on data you can audit?
Start with a pilot batch. You accept it before anything scales.