Builder · LLM Systems · AI Automation
I build production-grade systems around LLMs and automate workflows that actually matter.
Most of my work lives in the infrastructure layer — orchestration, state management, real-time processing.
The kind of code that makes AI systems reliable enough to ship.
📧 cloud.dhairya23@gmail.com ·
- Production-grade LLM-powered systems
- Multi-step reasoning & decision pipelines
- Real-time, context-aware AI
- The layer between “demo-ready” and “scale-ready”
- Event-driven automation powered by AI
- Workflows that reason, adapt, and recover
- Automation beyond chaining APIs — built to handle edge cases
- Reducing human effort without reducing control
- Behavioral signal analysis
- Anomaly & misuse detection
- Trust & safety tooling
- Systems that protect users quietly and effectively
🟢 Execution > Ideas
The world has enough AI demos. I care about systems that run, scale, and survive real users.
🟡 Infrastructure is Underrated
Models and prompts are the easy part. Reliability comes from orchestration, observability, and state management.
🔵 Simple Beats Clever
If future-me can’t debug it at 2am, it’s not good code.
Tool-agnostic. Problem-driven. I optimize for clarity, debuggability, and long-term maintainability.
🧭 Tool-agnostic. Problem-driven.
I optimize for clarity, debuggability, and long-term maintainability.
- LLM systems beyond chatbots
- AI-powered automation at scale
- Detection & safety systems
- Building tools that quietly create real impact
If you're working on:
- LLM systems that need to scale
- AI automation that actually saves time
- Trust, safety, and detection tooling
- Or just want to discuss what makes AI useful
Build · Ship · Iterate