Simplismart, a startup focused on AI inference infrastructure, has locked in $14 million in Series B funding to scale its software platform across global markets.
The round drew backing from Dallas Venture Capital, along with NVIDIA, Accel, Shastra VC, and Micromax. Simplismart plans to use the capital for platform development, deeper ties with cloud providers, and expansion into North America and sovereign AI projects.
The company builds the connective layer between AI models and GPU hardware. Its modular inference engine lets businesses fine-tune models for speed, cost, and performance. Meanwhile, its orchestration tools handle deployment and scaling across both cloud and on-premises environments.
“We have spent four years building the software layer between the model and the GPU, assisting customers optimise how their workloads are deployed and operated,” said Amritanshu Jain, co-founder and CEO. “This funding will allow us to take that platform to more enterprises and cloud providers globally.”
The startup reports that its annualized revenue run rate has multiplied 14 times over the past six months. Customers including HeyGen, Higgsfield, InVideo, Shiprocket, Apna, and Ema run production workloads through its platform for video, voice, multimodal, and enterprise AI applications.
Unlike competitors that buy or reserve GPU capacity and resell it, Simplismart owns no compute infrastructure. Instead, it supplies the software layer that lets third-party GPU fleets operate production AI workloads. As a result, cloud providers can offer managed AI services under their own brands without building the underlying stack themselves.
Dayakar Puskoor, managing partner at Dallas Venture Capital, pointed to the company’s technical strength and execution speed. He described Simplismart as enabling enterprises to run AI workloads more efficiently while helping cloud providers convert GPU capacity into scalable services.
The funding positions the startup to capture demand from enterprises and cloud operators seeking alternatives to owning costly inference infrastructure.
