Neysa Unicorn

Neysa's Velocis platform bundles training, testing, deployment and monitoring into one dashboard, avoiding the complexity of stitching together multiple point tools.

Positions itself against proprietary hyperscaler lock-in by building on open-source components at every layer.

Core target users training, fine-tuning and deploying AI models on India-based infrastructure.

Sector spread of Neysa's enterprise and government customer base.

New customers onboard and validate AI workloads on the Velocis platform working directly with Neysa's own Solutions Architects rather than pure self-service setup.

Beyond onboarding, Neysa positions itself as an ongoing strategic partner through a structured, multi-stage startup program that rewards continued commitment and growth.

Existing customers reach a named support channel for technical, billing and account issues rather than a generic ticketing queue.

Prospective customers can book a call to speak with a Neysa AI infrastructure expert who scopes the deployment and models total cost of ownership before production.

Customers scale GPU capacity directly through the platform's own console rather than via a sales-negotiated process.

Runs the Velocis platform that streamlines training, deployment and scaling of open-source-first AI workloads for enterprise teams.

Bare-metal/VM/Kubernetes access to high-end NVIDIA GPUs underpins the Velocis cloud.

Currently ~1,200 GPUs live; scaling capacity sharply using the new financing.

Blackstone plus co-investors Teachers' Venture Growth, TVS Capital, 360 ONE Asset and Nexus Venture Partners underwrite Neysa's infrastructure buildout via a majority equity stake.

Consumption-based pricing with discounts for longer-term reservations.

Neysa's CEO said the bulk of its new capital raise goes to deploying large-scale GPU clusters -- compute, networking and storage -- with a smaller portion directed to research and development.

Alongside the equity raised, Neysa plans to take on an equal amount of debt financing to fund its data-center and GPU buildout, adding a debt-service cost layer to its infrastructure spend.