AI Infrastructure at Scale has a Visibility Problem

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Full-stack AI acceleration cloud
Enforce security policy on all LLM endpoints
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Inside the Velocis architecture
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Unified Monitoring & Management
Live telemetry across GPU clusters
End-to-end MLOps, automated
AI Platform-as-a-Service (AI PaaS)
Train and scale AI on managed infra
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Deploy open-source LLMs managed endpoints
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NVIDIA & AMD GPUs on bare metal, VM, or K8
Protect AI environments and models

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Rethink underwriting and claims with AI
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AI for design, simulation, and smart factories
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Full-stack AI acceleration cloud
Enforce security policy on all LLM endpoints
Platform Architecture & Design
Inside the Velocis architecture
EXPLORE THE PLATFORM
Unified Monitoring & Management
Live telemetry across GPU clusters
End-to-end MLOps, automated
AI Platform-as-a-Service (AI PaaS)
Train and scale AI on managed infra
AI-native apps and agents, ready to deploy
Deploy open-source LLMs managed endpoints
Centralized control over your entire AI stack
NVIDIA & AMD GPUs on bare metal, VM, or K8
Protect AI environments and models

Tour the
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Technical Education & Research
AI Cloud for research labs and learning
Rethink underwriting and claims with AI
Scalable AI Cloud for AI-native teams
AI for design, simulation, and smart factories
Fraud, risk, and document AI for BFSI
AI for recommendations, pricing, and demand
Have a use case in mind?

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How customers build AI with Neysa
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AI teams often struggle with understanding the reasons behind rising costs and inefficiencies in infrastructure due to disconnected monitoring tools. Unified visibility is essential for effective cost management.
12 Jun 2026 • by Aishwarya Pattabiraman
Open source AI has made it easy to start. It has not made it easy to scale. As models grow in size and complexity, the infrastructure behind them starts to matter in a very real way.
03 Jun 2026 • by Isha Tilve
A fully managed real-time inference platform for open source LLMs, deployed inside India. Built by Neysa and Pipeshift for production AI workloads at scale.
27 May 2026 • by Divesh Sood
Fraud detection at most banks still happens after the transaction completes. Not because the models are slow. It’s because the infrastructure running those models can’t respond fast enough to catch fraud while the money is still moving.
25 May 2026 • by Sachin Nambiar