As India rapidly emerges as one of the world’s largest markets for inference-heavy AI workloads, a new partnership between Neysa and Pipeshift aims to address a critical gap in domestic infrastructure. The two companies have jointly launched a production-grade, real-time AI inference platform fully deployed within India, enabling enterprises to run AI workloads with lower latency, predictable costs, and complete data sovereignty.
The move comes at a pivotal time when enterprises across sectors are scaling AI adoption in customer service, software development, enterprise automation, and analytics. Despite the surge in demand, much of the infrastructure powering these workloads remains based overseas, creating challenges around latency, cost variability, and data routing.
Neysa, an AI compute and acceleration cloud provider, has integrated Pipeshift’s managed inference platform into its Velocis AI Acceleration Cloud. The combined solution delivers single-tenant, low-latency inference environments optimized for open-source models such as Llama, Mistral, DeepSeek, Gemma, and Qwen. Enterprises can deploy these models through OpenAI-compatible APIs without managing underlying GPU infrastructure.
“There is a clear line between AI that works in a demo and AI that works in production. Crossing that line takes more than a good model it takes infrastructure that keeps latency low and costs predictable at scale.”
— Arko Chattopadhyay, Co-Founder and CEO, Pipeshift
The platform is specifically designed for latency-sensitive applications including voice AI, enterprise copilots, search, workflow automation, and reasoning systems. By keeping all data, prompts, and inference processes within India, it addresses growing concerns around data sovereignty and compliance. Additionally, the infrastructure eliminates common bottlenecks associated with shared systems, such as rate limits, cold starts, and cross-region delays.
Karan Kirpalani, Chief Product Officer at Neysa, emphasized the efficiency gains delivered through the partnership. He noted that integrating optimized inference engines with dedicated infrastructure helps eliminate unpredictable token costs and latency issues, enabling enterprises to scale AI deployments more confidently.
Early adopters are already seeing tangible benefits. AI startup Nurix reported a threefold improvement in Time to First Token (TTFT) for its voice AI deployments, while Arrowhead AI successfully launched multilingual models with predictable latency in production environments.
With deployment timelines as short as two weeks, the platform allows organizations to transition from experimental AI use cases to production-scale deployments without rearchitecting systems.
The collaboration signals a broader shift in India’s AI ecosystem from reliance on external infrastructure to building sovereign, scalable AI capabilities that can support enterprise-grade innovation at home.