Amazon runs 600 million forecasts every day to move packages. Uber makes pricing decisions every few minutes across millions of rides. Walmart reprices 80 million+ items using predictive models.

These aren’t chatbot problems. They’re prediction problems — and they drive billions in real economic value.

Yet most enterprise AI spend today still goes to text generation. Summarize this. Draft that. Chat with a PDF. While generative AI gets the headlines, the decisions that actually move the economy — demand, pricing, inventory, revenue, churn — are still made with spreadsheets, gut feel, and models built for a different era.

What if every company could predict the future of their business with the same precision Amazon predicts package routing?

At Synthefy, we are building the most advanced intelligence for structured data in the world.

We’ve built the first foundation model purpose-built for structured data. Migas learns the language of time series — seasonality, trend, cross-domain transfer, and the relationship between what’s happening in the world and what’s happening in your data.

Our research pioneered multimodal time series forecasting. Today, Migas ranks #1 across 86 real-world datasets and beats every major baseline — Google TimesFM, Amazon Chronos, Salesforce Moirai — on state-of-the-art benchmarks.

In response to market demand, we combined research with application. Leading companies use Synthefy to forecast revenue, optimize pricing, plan demand, and predict churn. A $500M+ national retailer saw a 12% revenue lift. Samsung Semiconductor compressed a physiological model 300x while maintaining R²=0.86. The US Army, NetApp, and Deutsche Telekom are building on our platform.

Gartner predicts inference costs will drop 90% by 2030. But enterprise AI spend is going up, not down. 73% of companies already blew past their 2026 AI budgets.

This is Jevons Paradox playing out in real time — when something gets cheaper, we use dramatically more of it. As inference costs collapse, every company will be able to afford to run millions of predictions. The bottleneck won’t be compute. It will be: do you have a model that actually understands your domain?

Generic LLMs won’t solve this. You can’t prompt your way to accurate demand forecasting. You need models trained on the structure of time series.

The shift is coming. AI spend will move from generating text to generating answers. From “summarize my data” to “what’s going to happen next quarter.”

The companies that figure this out first won’t just save money. They’ll see the future before their competitors do.

We are backed by investors who believe structured data is the next frontier of AI. Our team comes from OpenAI, Stanford, MIT, NVIDIA, Microsoft, and UT Austin.

The future is too important to be left to chance.

Join us.