About Mvaak

Building the data layer behind intelligent systems.

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.

A labelling vendor applies a schema. We help decide what the schema should be, what counts as correct, and which cases the model will get wrong — then collect, annotate and validate to that standard.

We work across egocentric video, audio, video and text under one operating standard. Our most specialised work is first-person human demonstration data for Physical AI.

Founder

Shivam Pal

Founder & Managing Director

Shivam leads Mvaak’s work on the data layer behind intelligent systems — collecting, annotating and validating real-world information into datasets that hold up under training.

Company focus

Egocentric data for Physical AI
Data collection
Annotation
Quality assurance

Talk to the team

Why we focus on AI data

AI quality begins with data quality.

01
Quality is a definitions problem
Most bad datasets come from labels two people read two ways — fixed before collection, not after.
02
Coverage beats volume
The cases a model gets wrong matter more than the row count.
03
The guideline is part of the deliverable
Data arrives with the written rules it was built to, so it can be audited and extended.

Technical and operational capabilities

An execution company that runs distributed operations.

Real-world data collection is an operations problem as much as a technical one. Mvaak combines both.

Operating since 2023
A venture studio and execution partner with 100+ client engagements delivered across India, the UAE and Singapore.
Deep-tech in house
Our own AI/ML team builds and ships production systems, including government-funded deep-tech work. We understand what a training pipeline needs because we run them.
Distributed operations is the core skill
Recruiting, training, supervising and paying dispersed field teams across Indian states is the operational muscle the business runs on.
Governance as a design choice
Consent, wage floors and provenance were built into the operating model from the start rather than retrofitted.

Distributed collection operations

The network behind the hours.

An owned operations core plus a vetted agency network across India — capacity that can grow without a long ramp.

States covered: 6 → 18; Live today, with a defined ramp toward pan-India coverage
Trained collectors onboarded: 250; Network designed to scale within 90 days of programme start
Environment types: 40+; Household, commercial and agricultural settings
Languages: 12+; For paired audio, narration and instruction capture

Capacity position as of August 2026. Detailed ramp plan, rig inventory and coverage map available under NDA.

Quality and governance philosophy

Four things we hold to.

Governance is a design choice, not a retrofit: worker-level consent, a fair-wage policy and machine-readable provenance are part of how collection is run.

How quality and governance work