Indic data intelligence
Coremantle builds the high-quality, consented, and model-ready data infrastructure AI needs to understand Indic languages, dialects, accents, and real-world context.
The team
Now they’re building the Data Intelligence layer for Indic AI.
The Reverie team saw the problem firsthand: poor Indic data meant higher errors, wasted compute, weaker models.
After Reverie’s acquisition by Reliance Jio, they came back to solve it at the source. Coremantle is that missing layer.
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Founding team
The data infrastructure gap
Most of the world is underrepresented, usable data is scarce, and linguistic complexity compounds the gap.
Roughly 90% of the training data behind current generative AI systems is English, while only 18% of the world’s population speaks it. This leaves the majority of the world largely under-served.
What little data exists still needs heavy processing before a model can learn from it. Noise, PII, cross-talk and invalid speech must be removed, while code-switching, transcription, timestamps, dialect and context are accurately captured. Until then, it is raw data, not training-ready data.
India already has 22+ languages and 100+ dialects. Add code-switching, accents and domain vocabulary, and every Indic language becomes thousands of distinct training-data problems.
Sources: AI4Bharat ASR · Google Research, Language ID in the Wild · Ethnologue · Census of India 2011. The ~2,000-hour model-ready stage is illustrative.
The cost of bad data
Models discover the damage after the training run, when GPU cycles are already spent, and retraining becomes the cost.
The fix
Coremantle turns raw Indic data into verified, model-ready intelligence, with quality engineered into every step from sourcing to production.
Every dataset verified, certified, and benchmarked before delivery.
Native experts vetted for language, dialect, and domain - not crowd workers.
75% of the pipeline is already automated versus ~30% industry standard, including quality enforcement built directly into every stage.
100% of raw data sources are guaranteed to increase model performance - clear sourcing, filtration before ingestion, DPDP-compliant consent.
BFSI · HealthTech · Logistics · eComm · Legal · AgriTech
Only as good as the models underneath it
⚠ Fails for Indian context
See how Coremantle turns raw Indic data into model-ready intelligence.
Proven against the field
With Coremantle data, WER dropped from 76.9% to 9.4%, outperforming every benchmarked engine on the same test.
Quality by design
No cycles wasted training on data that never should have been in the set.
Clean source data means fewer errors baked into the model from day one.
Every batch is model ready, not a starting point for more cleanup.
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Big Tech
For frontier and sovereign AI labs building the next generation of models.
Enterprise
For companies building, fine-tuning or owning AI for their business.
Where we go next
Coremantle started with India, one of the world’s most linguistically complex markets. Now we’re taking the same data intelligence infrastructure across the Middle East, Southeast Asia and Africa to make AI work for more languages, accents, dialects and people.
What comes next
Coremantle is building the data layer for every way AI learns to read, see, hear and act. Starting with speech. Expanding into text, visual, physical and robotic AI.
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Start with data your model can actually train on.