Open models and an agent marketplace, running on Proximal Agentic Compute (PAC). Try the same bundle in Google Cloud, burst onto a partner NeoCloud, then run the identical stack in your own data center. Hybrid by design, open by default.
Bring models to your data instead of moving sensitive data around. Secure, efficient, and privacy-first.
Maximize inference efficiency with any model, powered by high-performance, made-in-India infrastructure.
Deploy and scale AI seamlessly across clouds with full control and data sovereignty built in.
The exact PAC + open-models stack runs wherever your data needs to live. Evaluate in Google Cloud, scale on a partner NeoCloud, then graduate the identical bundle into your own data center.
Provision a managed PAC sandbox in minutes. Validate models, agents and policy against real workloads before you commit hardware.
Spill over to a Proximal-certified NeoCloud for elastic agentic capacity — same bundle, pay-as-you-go, no hardware commitment.
Run the identical bundle on the infrastructure you already operate. Your data never leaves the building — experience is byte-for-byte the same as the cloud. Built on the VMware estate you trust, accelerated by Unity™.
A memory-centric 2U node where every enterprise workload sits next to HBM. A 50× roadmap to population-scale inference.
Explore Unity™ →The core of Proximal Agentic Compute isn’t just running models — it’s Compute.AI: a runtime built around a microkernel that decomposes every enterprise query, dispatches each part to the right silicon, and only spends premium dollars when quality demands it.
Business questions don’t map to a single model — they span all of an enterprise’s data. The Compute.AI microkernel decomposes each query into subqueries and dispatches every one to the silicon built for it.
Why memory-first wins: most subqueries are memory-bound lookups, not raw FLOPs. Co-locating SQL, Graph, Vector and LLM next to one pool of HBM is what makes running all four together — fast and private — actually possible.
Explore the Unity™ hardware platform →Once the microkernel decomposes a query, Compute.AI picks the right model for each subquery. Local open models handle the work cheaply on x86; when quality demands more, PAC transparently escalates to a public API model. Best answer, lowest defensible cost — automatically.
PAC treats inference as an optimization problem, not a fixed pipeline — matching each request to the cheapest model that clears the bar.
Per-request routing across your model fleet. PAC chooses the model at run time based on the prompt, latency budget and live capacity.
Local-first. Open models on your x86 run at near-zero marginal cost — PAC defaults to them whenever they can do the job.
Every local answer is scored. When it falls short of your quality threshold, PAC escalates to a public API model — only paying for premium when it matters.
The Proximal partner ecosystem brings purpose-built agents for the industries you run. Deploy them privately, on open models, on your own infrastructure.
Underwriting, fraud, compliance & complex transaction workflows.
Clinical documentation, imaging & administrative automation.
Citizen services and case management with data sovereignty.
Quality, predictive maintenance and supply-chain orchestration.
Yield, advisory and operations intelligence from field to market.
Coding, IT automation and digital workflow assistants.
The fastest path to enterprise AI isn’t a forklift — it’s the VMware estate already running your business.
Tens of thousands of enterprises already run VMware. PAC turns that footprint into a private AI platform: x86 + xPU agentic compute on the same vSphere clusters your team operates, with the same HA/DR, the same IAM, and the same change-management. No forklift, no parallel stack — AI delivered as an upgrade.
Activate AI on the vSphere clusters already serving your business — not a separate AI cluster.
Agentic workflows route across both. CPU handles most subqueries cheaply; xPU accelerates LLM reasoning where it counts.
High availability, disaster recovery and live migration — inherited from VMware, built in.
Govern AI with the same VMware automation & vSphere tooling your team already masters. PAC ships as the upgrade.
To democratize enterprise AI by providing secure, sovereign, and scalable infrastructure solutions that keep your data private while unlocking the full potential of artificial intelligence.
From Fortune 500 companies to government agencies, we’re trusted by organizations that demand the highest levels of security, compliance, and performance.
Renu is a serial entrepreneur and technologist with over three decades of experience spanning silicon, systems, in-memory computing, and cloud infrastructure. Proximal is his eighth venture, shaped by fifteen years of work at the intersection of India’s cloud ecosystem and open systems innovation.
Before founding Proximal, Renu built and led ventures including Terizza, Unity Microsystems, and InSilica, and held senior leadership roles at Sun Microsystems, SAP, and VMware. At VMware, he incubated VMware Cloud on Equinix and led technical innovations in ML and advanced memory systems. At SAP, he drove the consolidation of 23 global cloud stacks and helped architect large-scale in-memory database systems for the cloud.
Spin up in Google Cloud, burst onto a partner NeoCloud, or deploy to your own infrastructure — the bundle is identical.
The bundle is identical in all three. Pick a target and launch.
Coming soon near your location. Talk to our experts about deploying PAC on the VMware infrastructure you already own.
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