Platform Architecture
A modular, four-layer AI platform — every component built for sovereign, on-premises deployment. Fully indigenous stack.
Model Layer
Foundation and fine-tuned models trained on Indian data, optimized for Indian languages and contexts.
- Proprietary foundation models (multilingual)
- Domain-specific fine-tuned variants
- Indic language specialization (12+ languages)
- Continuous training and improvement pipeline
- Model evaluation and benchmarking suite
Knowledge Layer
Structured and unstructured knowledge management — RAG, vector search, and domain knowledge graphs.
- Retrieval-Augmented Generation (RAG) engine
- Vector database for semantic search
- Domain-specific knowledge graphs
- Document ingestion and processing pipeline
- Real-time knowledge base updates
Intelligence Layer
Orchestration, reasoning, and agentic capabilities that power complex, multi-step AI workflows.
- Multi-agent orchestration framework
- Task planning and decomposition
- Tool use and API integration
- Guardrails and safety systems
- Human-in-the-loop workflows
Deployment Layer
Secure, scalable infrastructure for on-premises and air-gapped deployment across any environment.
- On-premise deployment automation
- Air-gapped environment support
- Kubernetes-native orchestration
- Hardware-agnostic (GPU/CPU/TPU)
- Monitoring, logging, and observability
Architecture Principles
Modular by Design
Each layer can be deployed, scaled, and updated independently.
Sovereign First
No data leaves the deployment perimeter. Ever.
Hardware Agnostic
Runs on NVIDIA, AMD, Intel — or custom Indian silicon.
API-First
Every capability is accessible via standardized REST and gRPC APIs.
Observable
Built-in monitoring, logging, tracing, and audit trails.
Secure by Default
Encryption at rest and in transit. Role-based access. Zero trust.