Data Science Wizards (DSW) is building the Enterprise AI Operating System for regulated and hybrid enterprises – DSW UnifyAI OS – a governed system layer designed to operate AI securely and continuously inside global enterprises. The company will formally launch the OS in Mid-March.
As AI moves from experimentation into execution, it becomes a long-running system embedded within the enterprise itself, not just a platform or a tool. DSW enables enterprises to build, deploy, operate, and govern AI, ML, and agentic workloads continuously and safely, under real-world regulatory and operational constraints.
UnifyAI OS enforces governance-as-code at runtime, runs entirely within customer-controlled environments, preserves full ownership of enterprise-developed AI artifacts and their source code, and integrates across the AI ecosystem without structural vendor lock-in.
DSW is focused on making AI a dependable, governable part of the enterprise fabric.
This approach enables enterprises to:
-Run AI entirely within customer-controlled environments across on-prem, private cloud, and hybrid infrastructure
-Retain full ownership of models, agents, workflows, artifacts, and their associated source code
-Enforce governance during execution, not just at deployment
-Integrate external models and ecosystems without structural vendor lock-in
-Build and operate unlimited AI and Agentic use cases under a unified system layer – no more cost-per-use-case barrier.
Unlike fragmented AI stacks priced per model or per workflow, UnifyAI OS centralizes governance at the system level. This allows organizations to create new AI capabilities without rebuilding compliance frameworks or introducing new vendor dependencies each time.
“AI is no longer experimental for enterprises. It is becoming core to how decisions are made and operations are run,” said Sandeep Khuperkar, Founder and CEO, Data Science Wizards. “This shift requires an operating system that can govern AI during execution at enterprise scale, not just a collection of disconnected tools or platforms.”
The architecture separates control from execution. A non-bypassable kernel governs policy, lifecycle, and auditability, while managed runtimes execute machine learning models and agentic workflows within defined operational boundaries. Every action is traceable. Every workload operates within enterprise-defined policies.
“Enterprises need AI systems that are powerful, but also transparent, controllable, and reversible,” said Pritesh Tiwari, Founder and Chief Data Scientist, Data Science Wizards. “We built UnifyAI OS to ensure organizations retain control and sovereignty over their AI, including what use cases they build, how those systems operate, and where their data resides.”
The system layer is designed for highly regulated sectors such as banking and insurance, where runtime governance and auditability are critical, while also supporting enterprise-scale AI operations across telecom, healthcare, manufacturing, retail, and other industries.
The introduction reflects a broader shift in India’s deep-tech landscape, contributing foundational AI infrastructure for global enterprises. Not just application-layer innovation, but the system layer that will govern AI execution worldwide.