I build AI systems that actually work in production.

From messy inputs → structured actions → reliable execution.

Not demos. Real systems.

• 🧩 Multi-agent AI systems (planner → router → executor)
• 🔄 Workflow automation (email → data → action)
• 🧠 Memory-aware AI (RAG + context)
• ⚙️ Scalable backend + system design
• ☁️ Infra, deployment, and reliability

• Converts unstructured inputs into structured operations
• Routes tasks across specialized agents
• Executes workflows with logging + recovery
• Maintains full audit of AI decisions

SaaS-ready AI system for multiple businesses

• Domain-based tenant resolution (dynamic routing)
• Isolated knowledge + memory per tenant
• Public + private AI access layers
• Voice agent integration with Dialpad: system information retrieval + automated booking management
• Scalable backend for multi-client usage

Role: Backend & System Design
Focus: Multi-tenancy, clean architecture

Real-time system with active users

• RTMP streaming pipeline
• WebSocket-based chat
• Multi-user live sessions
• Backend handling concurrency

Backend: Python · Django · FastAPI · Node.js
AI: LangChain · LangGraph · OpenAI · RAG
Infra: Docker · Nginx · AWS · CI/CD
Data: PostgreSQL · Redis · MongoDB

• Building AI orchestration layers (like AI operating systems)
• Designing agent-to-agent communication systems
• Running LLMs locally + hybrid cloud setups
• Creating fault-tolerant AI workflows with audit logs

Systems that scale > scripts that work once

• Simplicity over complexity
• Reliability over hype
• Reusability over rebuilding