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Ground models in real operational context
We prioritize signals that come from live workflows, domain constraints, and real decision paths instead of abstract benchmarks alone.
We work on the layer between models and operations: training infrastructure, evaluations, RL environments, and AI-native systems built for work where judgment matters.
Our focus is on domains such as software engineering, healthcare, and finance, where model quality depends on real context, not generic proxies.
Zstate was built to carry that context through both training and production.
Four principles that shape every system we build.
We prioritize signals that come from live workflows, domain constraints, and real decision paths instead of abstract benchmarks alone.
Valuable systems help teams make better calls under ambiguity, especially where quality, trust, and edge cases matter.
Reliable AI needs tight feedback loops, robust measurement, and environments that reflect the work the system is expected to handle.
We care about systems that can carry context end to end, integrate with teams, and remain useful after the first impressive run.
Zstate brings together experience in building AI models, agentic systems, deep-tech companies, and commercial teams across India and global markets.
Vijay focuses on applied AI systems, agents, and the infrastructure that supports them, shaped by experience leading engineering at scale.
Himanshu brings founder and operator experience from 15+ years building and scaling deep-tech companies in India and globally, with a strong focus on rigor, technical depth, and practical outcomes.
Manuj brings 15+ years in GTM and commercial leadership, with experience building and scaling companies in India and globally, and a track record of evangelizing and selling AI-native products in competitive markets.
Whether you need domain intelligence or an agentic AI system, let's start with a conversation.