TL;DR: Structured data is the language of measurement and decision-making, yet AI still treats it as an afterthought. At Prior Labs, we’re building Multimodal Tabular Foundation Models (TFMs), starting with TabPFN, that understand tables natively—learning statistical reasoning directly from data. Our vision is broader: truly agentic AI systems capable of understanding high-level goals, fusing tables, language, and images to reason, integrate domain knowledge, infer causality, and adapt dynamically. This isn’t just better analytics—it’s a new foundation for discovery across science, medicine, and the global economy.
While artificial intelligence masters language and vision, it remains surprisingly inept with structured data. This isn’t niche data – structured tables are the language of measurement and empirical observation. AI now generates art and poems but struggles to natively comprehend the core operational data in spreadsheets and databases driving most critical decisions across medicine, finance, science and virtually all industries. This isn’t just a gap; it’s a massive bottleneck holding back progress.
Imagine, instead, a future where AI doesn’t just interact with tables through brittle tools, but understands them. A future where intelligent agents can instantly forecast market trends from financial logs, accelerate the discovery of life-saving drugs by interpreting clinical trial data, optimize global supply chains using real-time sensor readings, prevent billions of dollars in fraud by spotting anomalies in transactions, and personalize medicine based on genomic insights. This isn’t merely about better analytics; it’s about transforming how discovery happens, how businesses operate, and how we tackle grand challenges like cancer and climate change. It promises to reshape data science itself, from university curricula to organizational structures. At Prior Labs, we are building this future.
Today, we witness iterative cycles where domain specialists brief data scientists, who then wrestle with outdated models, aggregate findings, report back, and painstakingly refine questions or data inputs—a process ill-suited to the pace of modern discovery and business. While LLMs can call tools to interact with tables, they lack a deep, i
