On-Premise AI Architecture
A technical deep dive into NayaFlow's on-premise AI platform architecture, featuring Deep Agents UI, AWS MCP integration, and local LLM deployment with complete data sovereignty.
On-Premise Platform Architecture
NayaFlow's architecture is built for complete data sovereignty with Deep Agents UI providing enterprise-wide access, AWS MCP integration for cloud services, and local AI models eliminating external API dependencies.
Deep Agents UI Layer
Web-based visual interface accessible to 500+ employees through browsers, providing role-based access from executives to developers with no-code capabilities.
AWS MCP Integration
50+ Model Context Protocol servers providing direct access to AWS services like DynamoDB, Aurora, Bedrock, CloudWatch, and Cost Explorer without external API calls.
Local AI Models
GPT-OSS and open-source LLMs with Apache 2.0 licensing, deployed via Ollama with GPU acceleration, ensuring complete data sovereignty and unlimited usage.
Figure 1: High-level architecture of the NAYAFlOW platform
Deployment Architecture Options
NayaFlow supports multiple deployment architectures from single-server installations to multi-site global deployments, all maintaining complete data sovereignty.
Single-Server Deployment
Complete AI platform on one server for 5-50 employees with Deep Agents UI accessible via internal network only.
Components:
- •Deep Agents UI (Web Interface)
- •LangGraph Server (Orchestration)
- •Ollama + GPT-OSS 20B (Local AI)
- •50+ AWS MCP Connections
Investment: $5K-$8K hardware
Perfect for: Startups, proof-of-concept
High-Availability Cluster
Load-balanced multi-server architecture for 50-500 employees with 99.99% uptime SLA and geographic redundancy.
Architecture:
- •2x Web Servers (Load Balanced)
- •3x Application Servers
- •3x GPU Inference Nodes
- •3x Database Servers + Replicas
ROI: $734K/year savings vs cloud AI
Perfect for: Mid-size enterprises, 24/7 ops
Multi-Site Hybrid
Global deployment for 500+ employees with regional data sovereignty, offline capability, and automatic synchronization.
Global Architecture:
- •HQ: Full HA cluster deployment
- •Regional: Local replicas
- •Edge: Lightweight nodes
- •Works offline, syncs when connected
Benefits: Sub-50ms response globally
Perfect for: Global enterprises, regulated industries
Technical Implementation Details
LangGraph
State-of-the-art framework for building stateful, multi-agent applications with LLMs using a graph-based approach.
Key capabilities:
- Stateful graph execution
- Human-in-the-loop interactions
- Persistent memory management
- Advanced error handling
CrewAI
Framework for orchestrating role-based autonomous AI agents, designed for collaborative tasks with minimal code.
Key capabilities:
- Role-based agent design
- Pre-built agent templates
- Collaborative task execution
- Simplified agent communication
AutoGen
Open-source framework for building conversational AI systems with multiple agents that can work together to solve complex tasks.
Key capabilities:
- Customizable conversation flows
- Multi-agent conversations
- Human-in-the-loop integration
- Tool use and function calling
