Livestock & Wildlife AI Model
NandiBaba — India's Sovereign Foundation Model for Animal Health, Built on Field Data Rather Than Scraped Text
Sovereign
Training Corpus
Species
Aware Tokenisation
Hindi
First Interaction
2027–28
Commercial Target
General-purpose language models know a great deal about the internet and very little about a buffalo in Nalanda in August. NandiBaba is built the other way round — a decoder-only foundation model trained on field-verified Indian animal health data, with species tokens, an embedded drug-safety layer and Hindi-first voice interaction designed for a farmer holding a phone in one hand and a rope in the other. It is not a wrapper on someone else's model, and the training corpus is not purchasable.
Model Architecture & Design
Sovereign Foundation Model
A decoder-only transformer trained and controlled domestically, so the intelligence layer underneath India's livestock economy is not dependent on a foreign provider's pricing, availability or policy decisions.
Species-Aware Tokenisation
Dedicated species tokens allow the model to reason distinctly about cattle, buffalo, goat and sheep physiology, dosing and disease presentation rather than averaging across an undifferentiated animal category.
Embedded Drug-Safety Layer
Contraindication and dosage logic is built into the model's response path, so an unsafe suggestion is intercepted at generation rather than caught downstream — or not caught at all.
Hindi-First Voice Interaction
Voice-to-voice interaction in Hindi and regional languages, because the farmer who most needs this system is least likely to type an English query into a chat box.
Field-Verified Training Corpus
Trained on longitudinal, GPS-anchored, veterinarian-attributed field records from Indian conditions — a corpus that cannot be scraped, purchased or reconstructed retrospectively by a better-funded entrant.
Wildlife Extension
The same architecture extends to wildlife health and zoonotic monitoring, supporting conservation bodies and forest departments working at the interface where livestock, wildlife and human health meet.
How the Model Is Built and Deployed
Model development runs on a separate research track from the commercial platforms, funded by them rather than dependent on external capital for survival.
1
Corpus Construction
Field records from connected platforms are curated, de-identified and structured into a training corpus with veterinarian attribution and clinical validation.
2
Base Training
The decoder-only architecture is trained with species-aware tokenisation on dedicated GPU infrastructure, establishing domain grounding absent from general models.
3
Alignment
Supervised fine-tuning and preference alignment are applied with veterinary expert review, tuning the model toward safe, actionable field guidance.
4
Safety Integration
Drug-safety and contraindication logic is embedded into the response path and adversarially tested against unsafe prescribing scenarios.
5
Field Deployment
The model is deployed into triage and advisory workflows across connected platforms, where live use generates the feedback that improves the next iteration.
Who the Model Serves
Livestock Farmers
Households receiving first-line triage guidance in their own language at the moment an animal falls ill, before a provider can physically reach them.
Veterinarians & Paravets
Practitioners using AI-assisted differential support and safety screening as a second opinion, not a replacement for clinical authority.
Conservation & Forest Bodies
Institutions monitoring wildlife health and zoonotic spillover risk at the livestock-wildlife interface.
Research & Institutional Users
Academic and government bodies requiring a domain-specific model grounded in Indian species, breeds and field conditions.
Development status and limitation. The foundation model is under active development on a research track with a commercial target of 2027–28; deployed capability today operates within the triage and advisory layers of connected OYMOM platforms. AI output is decision-support only. It does not constitute a veterinary diagnosis or prescription, and clinical authority rests exclusively with a registered veterinarian under the Veterinary Council of India Act 1984. Training data is de-identified and processed under the Digital Personal Data Protection Act 2023.
DPIIT RecognizedStartup India
RKVY-RAFTAARRs. 25 Lakh Grant
NIAM JaipurMinistry of Agriculture
APEDA 2026Business Challenge
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