AI data infrastructure
Most data providers optimise for volume, but poor-quality data wastes compute and weakens model performance.
Coremantle combines contextual data, technology-enforced quality, and expert intelligence to help models learn more from every cycle.
The edge of available data
It's more intelligence per training cycle.
The valuable data isn't just more tokens or hours. It's rare, domain-specific, contextual, multilingual, conversational, and representative of how people actually use AI. Finding and sourcing that data at scale is difficult.
Audio, conversations, documents, and other raw corpora still need deduplication, transcription, segmentation, annotation, contextualisation, and quality validation before they can reliably improve a model.
Accents, dialects, code-switching, domain terminology, intent, cultural nuance, and edge cases are easy to flatten or mislabel in large-scale annotation. The model gets the words, but misses what they mean.
A bad label or noisy example doesn't stay isolated. It can affect training, fine-tuning, evaluation, and deployment, leading to weaker accuracy, unreliable behaviour, and more hallucinations. When training runs are expensive, discovering the problem after training means paying to learn the same lesson again. Every training cycle needs to count.
The missing data layer
Consent-first. Quality-engineered from source to model-ready output. Built to understand linguistic complexity before your GPUs ever see the data.
Proven against the field
Who we build for
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
A sovereign model needs a sovereign data foundation. Locally sourced, consented, traceable data across India’s languages and dialects.Sarvam · BharatGen · IndiaAI ecosystem
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
Continuous data infrastructure
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.
Real world. Real languages.
High-quality data for stronger models.
Human feedback and safety at scale.
Measure what matters. Find what’s missing.
Close gaps. Reduce errors. Improve performance.
Continuously expand. Always up to date.
Model v2.1 → v2.2
Detect what’s missing as requirements change.
Find real-world data across languages, domains, and contexts.
Curate, label, and validate data for quality, safety, and signal.
Continuously expand coverage as the model evolves.
Your model evolves. Your data infrastructure evolves with it.
The engine behind it
Where to start
We’ll build the language, domain, training or evaluation data needed to close the gap, then keep expanding it as your models evolve.