$25M Series B for AI-powered platform builder

Our focus has largely been on enterprise, while the slowdown has mainly affected mid-market companies and startups.” Insight Partners managing director Lonne Jaffe, a board member at Deci, added in an email with TechCrunch: “Deci’s powerful technology lets you input your AI models, data, and target hardware — whether that hardware is on the edge or in the cloud — and guides you in finding alternative models that will generate similar predictive accuracy with massively improved efficiency … [It’s a value add because] having a more efficient infrastructure for AI systems can make AI products qualitatively different and better, not only cheaper and faster to run.” Deci, a startup company with 50 employees who are developing a platform to build and optimize AI-powered systems, today announced that it closed a $25 million Series B financing round led by Insight Partners with participation from Square Peg, Emerge, Jibe Ventures, Fort Ross Ventures, and ICON that brings the company’s total raised to $55.1 million. “Deci’s proprietary technology [can generate] new image classification models that … deliver more than 2x improvement in runtime, coupled with improved accuracy, as compared to the most powerful models publicly available,” Geifman told TechCrunch in an email. Companies face several hurdles in creating text-, audio- and image-analyzing AI models for deployment across their apps and services. Deci isn’t unique in this — Google’s Vertex AI service leverages NAS to optimize the performance of models on specific, customer-specified tasks. The funds will be used to expand Deci’s go-to-market activities as well as support the company’s R&D efforts, according to co-founder and CEO Yonatan Geifman. “The business impact of this ability translates into … shortening time to production and the ability to unlock new AI use cases and address new market segments on resource-constrained devices.” Geifman also notes that compressed models can help companies save on inference compute costs — that is, the costs of actually serving models once they’ve been deployed. Moreover, Deci also competes with a number of companies developing ways to make models more efficient, like OctoML, Neural Magic and OmniML. (TechCrunch). Continue reading.



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