AI Impact Summit 2026: Proximal.ai Builds AI Where Data Stays
At the India AI Impact Summit 2026, “sovereign AI” wasn’t pitched as a patriotic slogan—it was treated as an engineering brief. In on-stage remarks and conversations at the event, Renu Raman, founder and CEO of Proximal Cloud, outlined Proximal.ai’s approach to enterprise and sovereign AI, anchored by partnerships with AMD, NxtGen, and E2E Networks. The company’s proposition is straightforward: bring compute and intelligence to the edge of where data already resides—inside hospitals, universities, government agencies, and agricultural operations—rather than dragging sensitive data to distant clouds.
Sovereignty, Defined by Control—not Coordinates
Raman argued that India’s sovereign AI ambition should be seen as a spectrum of control across the stack, not a binary box to tick. Hosting compute inside national borders, he cautioned, is necessary but insufficient. True sovereignty rests on who owns and operates the system-level levers.
- Control plane and orchestration
- Identity, access, and key management
- Telemetry, logs, and observability data
- Update pipelines and support channels
- Hardware and software supply chains
When any of these dependencies sit outside domestic authority, sovereignty weakens. Data leakage isn’t just about databases; it also involves who can see prompts, metadata, logs, and interactions—and under which jurisdiction. The message was clear: operational authority, not geography, is what makes an AI stack sovereign.
Building Where Enterprise Data Lives
Proximal.ai positions itself as an application infrastructure layer that sits close to enterprise data sources. The company says parts of this intermediate layer can be open-sourced and locally managed to strengthen control without waiting for long-horizon hardware independence.
Raman noted that building semiconductor fabs is a decades-long journey, but India can move faster higher up the stack. For defence and government, reducing reliance on external systems is strategic; in the near term, application-layer sovereignty offers a pragmatic path.
Scale and the Hardware Question
He pointed to the economics of data centers: each gigawatt of power equates to significant hardware value creation. Can India support a large-scale hardware ecosystem? “Why not?” he suggested, framing it as a long-term opportunity aligned with near-term software and application control.
The 80% Untapped: Unlocking Enterprise Data
Raman compared today’s AI inflection to India’s telecom leapfrog from landlines to mobile. Only about 20% of the world’s data has been touched by AI models, he said; roughly 80% sits within enterprises—often unstructured and underutilized. The constraints aren’t just power budgets or latency—they’re also about unlocking this dormant data responsibly and efficiently.
Why Private AI Infrastructure
- Economics: Public cloud is ideal for spiky or experimental workloads. But when AI usage becomes continuous and predictable, owning or reserving capacity can optimize total cost.
- Speed: Proximity to data reduces round trips and delivers real-time responsiveness, a requirement for mission-critical applications.
Compliance Without Stalling Innovation
On regulation, Raman took a middle path. Compliance protects citizens and national interests, but rigidity can freeze progress. The imperative, he argued, is to make compliance, innovation, and business outcomes reinforce one another—especially as India seeks to replicate the kind of leapfrogging seen with UPI and mobile connectivity.
Readiness Is a Spectrum, by Business Size
Enterprises won’t adopt AI at the same pace or in the same way. Readiness varies across small, medium, and large organizations—and it’s not just about model accuracy. It spans processes, integration with existing systems, staff capability, and the ability to iterate safely. Reference architectures help, but cookie-cutter blueprints rarely fit complex realities; flexibility is essential.
Data Quality, Pragmatically Managed
Perfection isn’t the goal. According to Raman, modern compute and models can automate significant portions of data transformation, labeling, and cleansing that historically delayed AI projects. The outcome: organizations can reach an “80th percentile” readiness with a human-in-the-loop to handle edge cases and governance.
Security: Keep Data Inside, Broaden Access Controls
Proximal.ai is developing a gateway-style appliance that enforces strict data perimeter rules while enabling controlled access to internal models or external APIs. The design aims to ensure that enterprise data doesn’t leave operational boundaries—even as teams experiment with different models and tools.
At the same time, Raman expects public cloud isolation to keep improving. The future won’t be an either-or choice between on-prem and cloud. “The answer is both,” he said, pointing to a hybrid architecture where sensitive workloads stay close to data, and elastic capacity complements them when needed.
From Verticals to Control
At the summit, Proximal.ai demoed live use cases across healthcare, education, and AgTech. Yet the real headline was architectural: the national conversation is shifting from model benchmarks to infrastructure authority, from cloud convenience to deliberate control. Sovereignty, in this telling, is less a location pin and more a design principle applied layer by layer.
“Sovereignty is not a checkbox.”
That line may define the next phase of India’s AI journey. As the industry races ahead, competitive advantage won’t come only from better models—but from who controls the systems around them: identity, telemetry, updates, and the pipelines that keep AI running where the data lives.
