Built close to the work.
Nond works inside important, ambiguous problems where the right workflow, product shape, and AI architecture have to be discovered through execution, not decided in a deck.
How Nond works
Nond stays close to the workflow while the system is being shaped and built. That means understanding the business process, existing systems, data access, permissions, edge cases, and rollout constraints before deciding what the AI system should do.
The work is carried by an AI-native pod of experts who understand enterprise needs: reliability, security, integrations, controls, adoption, and the practical constraints that decide whether a system gets used.
About Gaurav
Gaurav is an AI and platform engineering leader who has built agentic products, internal AI systems, API platforms, workflow automation, and enterprise software infrastructure.
Before Nond, he built and led AI product and platform work across startup and enterprise environments: agentic commerce systems, internal AI tools, API platforms, workflow automation, secure execution infrastructure, and production engineering programs. Earlier, as Platform Architect at Icertis, he worked on enterprise systems and workflows used by companies such as Microsoft, Apple, Mercedes, HP, and others.
That background shapes Nond's bias: AI systems should be designed around real workflow constraints, measurable reliability, controlled access, and the practical edge cases that appear only when software meets business reality.
Relevant experience
- Hybrid agent-workflow architecture for production AI products.
- LLM evaluation across cost, quality, and latency tradeoffs.
- Enterprise API platforms, gateways, and multi-tenant architecture.
- Secure execution, observability, CI/CD, validation, and rollout discipline.
Why Nond exists
Tools are easy to start with. Useful AI systems are harder because the work depends on messy inputs, existing systems, permissions, exceptions, approvals, and human judgment.
Nond exists for that middle space: where a company knows the problem matters, but the right workflow, architecture, and implementation path need to be discovered through close execution.
- Understand the workflow before choosing the AI pattern.
- Design around system access, controls, and failure modes.
- Build with the client team, close to real edge cases.
- Harden the system for adoption, reliability, and handoff.