Every platform shift in AI so far has been narrated in language. We prompt models in words, we read their answers in words, and the public imagination of AI runs almost entirely on text and speech. All the while the largest data modality on earth sits mostly untouched. Numbers, the raw output of machines, meters, and markets, pour out without pause while quietly driving how the physical and financial economy operates.
The scale here is hard to overstate. Google processes roughly 3.2 quadrillion language tokens a month while the world produces thousands of times more number tokens than that. Each of those observations is to a number token what a word is to a language token, and they accumulate every second of every day, because the machines and markets producing them never idle. And these tokens keep arriving around the clock because the operations that generate them never stop.
Each of those numbers is a decision waiting to happen. Buried in that stream is a warning the business needs: equipment heading for failure, inventory about to run dry, a payment that should never clear. And the value sitting on that data is enormous and largely unrealized. The cost of missing those signals runs into the trillions. Sharper forecasts can claw much of that back, which hinges on data that current models simply cannot parse.
This is the opportunity Synthefy was built for. We are proud to share that Wing led Synthefy's $6.5 million seed round to build what the team calls Structured Data Foundation Models (SD FMs): foundation models for the world's structured data. Language already has its model and numbers are next.
The team behind Synthefy: AI researchers from Uber, Stanford, and NVIDIA
Synthefy is the kind of team that will win a category like this with an applied research group that has lived inside the numerical problem for years.
Somi Agarwal, Synthefy’s co-founder and CEO, worked on autonomous systems at Uber's ATG division and then went to UT Austin for a PhD focused on machine learning for time series. His research produced Time Weaver, an early paper on multimodal time-series models that anticipated the approach Synthefy now productizes at scale.
Sandeep Chinchali, co-founder, is a professor at UT Austin and a Stanford CS PhD who advised Somi's doctoral work. He is actively contributing to Synthefy’s research, a signal of how convinced this group is that the research moment has arrived.
Raimi Shah, co-founder and CTO, brings systems depth from Zscaler and NVIDIA.
In a category where research is the hard problem, the quality of the team's judgment is the product. This group has been asking how to teach a model to read numbers since before there was a market for the answer.
From research to shipped models: How Synthefy's Nori model outperforms existing ML pipelines
Synthefy did what the strongest research-led companies do. They published and shipped in the open, and the results speak for themselves.
Nori, their tabular model, launched with open weights and open training code. It ranks first across 130 public benchmarks — ahead of the previous state of the art — at a tenth the size of its nearest peers. It also runs in seconds on a single GPU with no training required. In its first weeks it drew 600,000 downloads and more than 5,000 Python installs.
Two capabilities set this work apart. First, Synthefy’s models can make predictions on a new dataset out of the box. Point Nori at a table and it produces predictions in seconds, without customer-specific feature engineering or model training. The work of building and tuning a bespoke ML pipeline becomes an API call.
Second, the models can combine structured data with unstructured text. In financial markets, they can read stock prices and trading data alongside earnings reports, regulatory filings, and news. In industrial operations, they can pair sensor readings with maintenance logs and incident reports. This brings the context behind the numbers into each prediction and moves Synthefy toward one pretrained model that works across financial, commercial, and operational problems.
Synthefy's opportunity: Teaching AI models to read numbers at scale
Most companies still turn structured data into predictions through a patchwork of task-specific systems. High-value problems get bespoke machine-learning models; many others remain in spreadsheets, business rules, or packaged software. Each new dataset and use case requires another round of setup, tuning, and maintenance. A foundation model that reads tables the way an LLM reads language replaces that repeated work with one shared, pretrained model.
The pull is already demonstrating traction. Customers across retail, finance, observability, and defense are running Synthefy for demand forecasting, pricing optimization, and failure prediction. Much of the demand arrives organically, from teams in fraud, retail, and industrial operations all asking the same question: can this model beat the one we built in-house on a use case that matters? Partnerships with Baseten andInfluxDB with integrations with Snowflake, and AWS put the models directly into the platforms where that numerical data already lives. The largest technology companies are beginning to build in this direction too, which tells us the category is real. We believe the winning foundation for numbers will be open and independent, putting the model directly in builders’ hands so any team can point it at any numerical problem.
Building the infrastructure layer for AI-driven numerical prediction
Wing has invested in the data and AI infrastructure layer through every recent platform shift, and we recognize this shape. A research-led team with a data advantage that compounds, building a horizontal foundation and selling first to the most demanding buyers, tends to own the layer everyone else builds on once the category matures. We saw that pattern in Synthefy and we’re proud to lead this round.
We believe this will become one of the defining infrastructure layers of the coming decade. It will sit quietly beneath demand plans, maintenance schedules, pricing engines, and fraud defenses at companies most people never think about, doing work that rarely makes headlines and matters enormously. Synthefy has the research depth, early traction, and conviction to build it. We’re proud to partner with Somi, Sandeep, Raimi, and the entire team as they teach machines to read the language of numbers.
