01
Underserved regions
More than 60% of the world speaks languages AI barely has data for. Across South Asia, Southeast Asia, MENA, and Africa, the corpus is still too thin.
Training data by region
Indic data intelligence for enterprise
The Indic data vacuum ends here.
Coremantle provides training-ready data built for higher accuracy, fewer wasted cycles, and production-grade AI for your enterprise.
The data infrastructure gap
The world’s AI ambition is outrunning its data.
Billions of people use languages that today’s models barely hear. The problem grows from scarcity into complexity, wasted compute, and compliance risk.
02
India’s languages are still missing
For every 54 hours of English speech data, there is roughly one hour of Hindi. Models inherit that imbalance.
54 : 1 data gap
03
Complexity is exponential
India’s 22 scheduled languages are only the start. Dialects, code-switching, accents, and domain vocabulary multiply the combinations.
Language complexity
04
Noisy data burns compute
Bad data consumes expensive GPU cycles. Teams often discover the quality problem only after training, when time and budget are already gone.
GPU burn
05
Compliance changed the market
Legacy datasets were not collected for DPDP-era requirements. Coremantle designs consent, provenance, and auditable workflows in from day one.
Consent first
Sources: Hugging Face Open ASR Leaderboard · Ethnologue 2024 · UN World Population Prospects 2024 · Census of India 2011
The missing infrastructure
So, we built the data infrastructure AI has been missing.
Using real Indic natural conversational data, we turn weak model performance into production-ready accuracy.
In our benchmark, accuracy improved from 23% to 91% with just 500 hours of customer call data.
Model benchmark
Quality by design
We engineered quality into every layer.
From compliant sourcing to a tech-first platform and a curated community of native experts, every layer is designed to eliminate error before it reaches the model.
- 04
Model-ready output
Every dataset verified, certified, and benchmarked before delivery.
- 03
Curated community
Native experts vetted for language, dialect, and domain — not crowd workers.
- 02
Tech-first platform
75% of the pipeline is already automated versus ~30% industry standard, including quality enforcement built directly into every stage.
- 01
Raw data sourcing
100% of raw data sources are guaranteed to increase model performance — clear sourcing, filtration before ingestion, DPDP-compliant consent.
EnterpriseYour business
BFSI · HealthTech · Logistics · eComm · Legal · AgriTech
→
Agentic AI layerRoutes · Decides · Responds
Only as good as the models underneath it
←
API vendorsGeneric ASR · LLM · TTS
⚠ Fails for Indian context
live call data ↓⇅ replaces generic APIs
Coremantle data intelligence layer
Your call data
Annotate & verify
Train custom model
Custom ASR · SLM · TTS
The result is verified, model-ready Indic data built for production AI.
Where language complexity matters
Built for the industries where context is critical.
Banking and financial services
Train AI on real financial conversations, terminology, accents, and customer intent.
Healthcare
Build language models that understand clinical context, patient speech, and regional variation.
Legal
Prepare AI for complex legal language, domain vocabulary, and multilingual documentation.
eCommerce
Improve search, support, and conversational AI across the languages customers actually use.
Logistics
Train voice and agentic systems for high-volume, multilingual operations in the real world.
AgriTech
Make AI understand farmers across regional languages, dialects, and agriculture-specific vocabulary.
Get started
Choose how you want to build.
Get training-ready data and let us build the agentic solution, or take the data and build it your way.