About Mvaak
Building the data layer behind AI.
Models are judged on their outputs, but they are shaped by their inputs. Mvaak works on the inputs — collecting, annotating, validating and structuring real-world information into datasets that hold up under training.
Who we are
An AI data services company, not a labelling vendor.
The distinction matters. A labelling vendor takes a schema and applies it. The harder and more valuable work usually happens before that: deciding what the schema should be, what counts as a correct example, and which cases the model is going to get wrong if nobody thinks about them now.
Mvaak works across four data types — egocentric video, audio, video and text — under one operating standard, so a project that spans more than one of them produces a corpus that stays internally consistent.
We work with teams building and training AI systems: founders and CTOs scoping a first dataset, ML engineers who know exactly what is missing, and data operations leads who need capacity without renegotiating quality.
What we believe
AI quality begins with data quality.
Three positions that shape how every engagement is scoped.
Data quality is a definitions problem
Most bad datasets are not the result of careless work. They are the result of a label set that two reasonable people can read two ways. Fixing that costs a conversation before collection and a re-annotation afterwards.
Volume is the easiest thing to measure
It is also the least predictive of whether a model improves. Coverage of the cases the model actually gets wrong matters more than the row count, and it is harder to sell, which is why it is often skipped.
The guideline is part of the deliverable
A dataset handed over without the rules it was built to is difficult to extend and impossible to audit. We deliver the written definition alongside the data, including how the awkward cases were decided.
Human + machine
Automation handles the mechanical part. People handle the part that decides.
Tooling is good at throughput, at flagging structural problems and at catching the errors that follow a pattern. It is not good at the examples that sit between two labels, where being right requires understanding what the model is for.
Those cases are a small fraction of any dataset and a large fraction of its value. They are where a schema gets tested, and where a guideline either holds or turns out to be underspecified.
So the division of labour is deliberate: structured process where process works, trained human review where it does not, and escalation rather than guessing when neither is enough.
Our approach
Real world in, AI-ready data out.
The same sequence on every engagement, so the last stage is predictable from the first.
- 01
Real World
Where the signal originates
- 02
Collect
Captured or sourced to specification
- 03
Annotate
Structured against a written guideline
- 04
Validate
Reviewed by people, not just scripts
- 05
Structure
Shaped into your schema
- 06
AI-Ready Data
Loads into your training pipeline
Our principles
Four things we hold to.
Quality
Built into the lifecycle rather than inspected at the end — a written definition of correct, work produced against it, and human review measured against the same definition.
Reliability
The same standard applied on the last batch as the first. Consistency across a corpus is what makes it trainable; consistency over time is what makes a partner useful.
Scalability
Workflows designed to hold their standard as volume grows, rather than trading accuracy for throughput once the pilot is signed off.
Technical understanding
Scoped around how models are trained and evaluated, so the output loads into your pipeline instead of needing to be reshaped on arrival.
Where we are
Based in New Delhi. Working with AI teams anywhere.
New Delhi, India, 110015
Have a data challenge?
Let’s build the dataset behind it.
Tell us what you’re building, what data you need and the scale of your project.