翻訳待ち：Exclusive: Synthefy raises $6.5M for its number-crunching models trained on numerical data instead of words

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A startup called Synthefy Inc. said today it’s going to for numbers what large language models did for words after raising $6.5 million in seed funding today. The funds will help to expand its new foundation-model platform that’s fine-tuned specifically for numerical data rather than text. The round was led by Wing Venture Capital and saw participation from Haystack, Samsung Next, Canonical Crypto and Lightscape. Angel investors from OpenAI Group PBC, Microsoft Corp. and Meta Platforms Inc. also backed the company. Synthefy is the pioneer of what it calls “Structured Data Foundation Models” or SDFMs. Similar to how LLMs learn to reuse word patterns by digesting vast amounts of text-based data, Synthefy’s SDFMs use numerical data to learn about numbers and calculations. By ingesting vast amounts of number-crunching data, it’s able to preserve the intricate relationships within time-series data and tables. This enables them to generalize across numerical data and perform calculations faster and more accurately than standard machine learning models, the company says. In addition to announcing today’s funding round, Synthefy also discussed its first open-source SDFM, which was quietly released a few weeks ago. It’s called Nori, and it has an extremely lightweight architecture, but despite this it packs one hell of a punch. In testing, a 30-million parameter version of Nori was able to outperform Google LLC’s 1.6-billion parameter TabFM model. It performs even better when its “Thinking” capabilities are enabled, surpassing Google’s model despite being just 2% its size, the company said. The company says Nori and other SDFMs are designed to tackle workloads such as fraud detection and dynamic pricing. These are not new applications. Traditionally, enterprises use machine learning frameworks such as LightGBM and XGBoost to handle these tasks, but Synthefy co-founder and Chief Executive Somi Agarwal told SiliconANGLE that most enterprises have to spend weeks on data preparation, training and fine-tuning to create models that can perform reliably enough. “That work does not compound,” he said. “Each new fraud, pricing or forecasting problem starts again. But with Nori, a team can point the model at a new table or problem and get a strong prediction without training or tuning a new model for that dataset. That can bring the initial evaluation down from weeks or months to just minutes.” According to Agarwal, SDFMs can be further enhanced and gain a considerable performance advantage when they are pretrained on specific tasks. “Nori has learned from millions of synthetic datasets, so it comes to a new problem with experience rather than starting from zero,” he said. “Even a small improvement in pricing, fraud or demand prediction can be worth a great deal at enterprise scales.” With the launch of its reusable foundation model layer, Synthefy is giving companies a way to train and deploy specialized models for basically any application that requires extensive number crunching. Things like demand forecasting, pricing optimization, risk analysis and infrastructure monitoring can be done faster and more accurately with SDFMs, Agarwal said. Enterprises can access these capabilities through an open model, a managed application programming interface or by deploying within their own computing environments. Looking ahead, Synthefy will use the funding to accelerate its research efforts, hire more engineers and build the next generation of Nori. In addition, it’s also open to new industry partnerships. It should have a few takers, considering that the first version of Nori has already been downloaded more than 600,000 times, just weeks after being made available. Though its models are open source, Synthefy believes that it can make money by offering premium support and capabilities atop its basic foundation layer. “The commercial opportunity is the enterprise product around the model, such as managed API usage, proprietary features, private deployments, security and governance controls, integrations, support and large-scale production infrastructure,” Agarwal said. Wing Venture Capital’s founding partner Gaurav Garg said he’s betting on Synthefy’s structured data models to become the next major expansion of the AI model market. “Synthefy is building a model platform that can address some of the largest and most valuable datasets in the world,” he explained. “Its combination of technical performance, efficient architecture, open models and early enterprise traction positions the company to define this emerging category.” Image: SiliconANGLE/Gemini A message from John Furrier, co-founder of SiliconANGLE: Support our mission to keep content open and free by engaging with theCUBE community. 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