coremantle.

AI data infrastructure

The data layer for the next billion AI users.

High-quality Indic-native data programs for frontier models, sovereign AI and voice infrastructure. Built for continuous training, evaluation, and model improvement.

Build your Indic data programSee why now

The edge of available data

Your model has reached the edge of available data.

Scarcity, linguistic complexity, poor quality, and compliance gaps now translate directly into wasted compute and weaker model performance.

01

Indic languages have a data vacuum

India’s languages are spoken at massive scale. Yet there are 54 hours of English data for every 1 hour of Hindi.

Training data by region
02

The complexity is exponential

22 languages is only the starting point. Dialects, code-switching, accents, and domain vocabulary multiply the data requirement.

Language complexity
03

Quality breaks the model

Noisy, inconsistent, or poorly annotated data teaches the model the wrong patterns. The error enters long before the model does.

Data quality
04

Bad data burns good compute

Every bad input still consumes expensive GPU cycles. The problem often becomes visible only after the training run is complete.

GPU burn
05

Compliance changed the data supply

Most legacy datasets were not built for the DPDP era. CoreMantle starts with consent, provenance, and auditability by design.

Consent first

Sources: Hugging Face Open ASR Leaderboard · Ethnologue 2024 · UN World Population Prospects 2024 · Census of India 2011

The missing data layer

So we built the data layer models can trust before training.

Consent-first. Indic-native. Quality-engineered from source to model-ready output. Built to understand linguistic complexity before your GPUs ever see the data.

Consent-first DPDP compliant sourcingQuality engineered upstreamCertified model-ready output
>95%Dataset quality
<5%Word error rate
>65%Automated QC
CertifiedModel-readiness certification

Proven against the field

And we beat every major speech-to-text engine while at it.

Model benchmark

Who we build for

One infrastructure layer for different AI ambitions.

01

Frontier AI Labs

Every new model release creates a new data requirement. CoreMantle keeps the training, evaluation, and alignment data moving with it.OpenAI · Anthropic · Google · Meta · Microsoft

Frontier labs
02

Sovereign AI

A sovereign model needs a sovereign data foundation. Locally sourced, consented, traceable data across India’s languages and dialects.Sarvam · BharatGen · IndiaAI ecosystem

Sovereign AI
03

Core Voice AI Infrastructure

Real conversations are where speech models break. We capture the accents, dialects, code-mixing, noise, and domain language benchmarks miss.Deepgram · ElevenLabs · Indic speech platforms

Voice AI

Continuous data infrastructure

Built for continuous improvment.

CoreMantle supplies the training, alignment, evaluation, and refresh data your models need at every stage. Not a one-time dataset. A continuously evolving data infrastructure for continuously evolving models.

  1. 01

    Source

    Real world. Real languages.

  2. 02

    Train

    High-quality data for stronger models.

  3. 03

    Align

    Human feedback and safety at scale.

  4. 04

    Evaluate

    Measure what matters. Find what’s missing.

  5. 05

    Improve

    Close gaps. Reduce errors. Improve performance.

  6. 06

    Refresh

    Continuously expand. Always up to date.

Your modelNew release ships

Model v2.1 → v2.2

New data needsRequirements change
New dialects New domains Failure modes Safety gaps
Coremantle continuous data layer

Identify gaps

Detect what’s missing as requirements change.

Source

Find real-world data across languages, domains, and contexts.

Build & verify

Curate, label, and validate data for quality, safety, and signal.

Refresh data

Continuously expand coverage as the model evolves.

Updated data flows back into the model
Training data
Alignment data
Evaluation data
Refresh data

Your model evolves. Your data infrastructure evolves with it.

The engine behind it

A data pipeline built to keep producing.

6,000+ hours procured
700+ vetted annotators
12 language clustersHundreds of dialects
5,000 hrs / year / languageSustained sourcing capacity
Language-agnostic platformOne workflow. Always improving.

Where to start

Tell us where your model breaks.

We’ll build the language, domain, training or evaluation data needed to close the gap, then keep expanding it as your models evolve.

Build your data programTalk to us