Deploy LLMs, RAG systems, and computer vision models on your own servers — not foreign cloud APIs. Full data sovereignty, 80% lower costs, and AI that speaks your users' language.
All models trained and deployed on your infrastructure. Your data never leaves your network.
No proprietary model lock-in. We use LLaMA, Mistral, Phi, and other open models you own.
Models fine-tuned for Hindi, Tamil, Telugu, Marathi, and 6 other Indian languages.
On-premise AI vs. cloud API costs — typical enterprise saves ₹2–5 Cr annually.
End-to-end AI development from model selection and fine-tuning to production deployment and ongoing operations.
Fine-tune open-source LLMs (LLaMA, Mistral, Phi) on your proprietary data. Run on-premise — zero data leaves your network.
Retrieval-Augmented Generation systems that give your AI accurate, up-to-date answers from your documents, databases, and APIs.
Domain-specific copilots for finance, legal, HR, and operations — integrated with your existing tools via APIs.
Invoice processing, quality inspection, medical imaging, and document extraction — 95%+ accuracy, production-ready.
CI/CD for ML models. Automated retraining, drift detection, A/B testing, and model governance on Azure ML / AWS SageMaker.
Deploy AI models on edge devices and on-premise servers — no cloud dependency, full data sovereignty, sub-50ms inference.
Credit underwriting AI, fraud detection, customer service chatbot in regional languages
Clinical notes summarisation, drug interaction checker, radiology report generation
Defect detection via computer vision, predictive maintenance, supplier document processing
Contract review automation, regulatory compliance checking, legal research assistant
Product description generation, customer intent analysis, inventory demand forecasting
Citizen query handling in local languages, document digitisation, policy summarisation
Challenge: Customer service agents spending 40% of time searching policy documents
Solution: RAG-based internal knowledge assistant trained on 50,000+ policy documents, integrated with existing CRM
Challenge: Manual quality inspection causing 3–5% defect escape rate on production line
Solution: Computer vision system with custom-trained defect detection model deployed on edge hardware
Only if you explicitly want us to. Our default approach uses open-source models (LLaMA, Mistral, Phi) deployed on your own infrastructure, ensuring 100% data privacy and zero ongoing API costs.
ChatGPT is a general-purpose model. We build domain-specific AI trained on your data, integrated with your systems, and deployed in your environment — delivering 10× more accurate and relevant results for your specific use cases.
For most enterprise use cases, a single NVIDIA A100 or H100 GPU server is sufficient. We help you size the infrastructure correctly and can deploy on your existing servers, private cloud, or co-location facility.
A focused AI pilot (single use case, production-ready) typically takes 8–12 weeks. Full enterprise AI platform deployments take 4–6 months. We always start with a 2-week discovery sprint.
Book a free AI Strategy Session. We'll identify your top 3 AI opportunities and present a 90-day roadmap with ROI projections.