Our mission is to turn real-world business problems into outcomes—by shipping AI systems people can rely on.

We started Softmax Data in 2019, at the moment Deep Learning models made it obvious AI was about to change fundamentally. We chose the name "Softmax" on purpose: it turns raw signals into probabilities—many possible paths. That's what we wanted to enable for clients too: more options, faster execution, better experiences—grounded in reality, not hype.

Our early work with Clio set our standard. We used entity linkage to solve a tangible problem: massive duplicate leads wasting time and resources. It wasn't a "cool AI feature." It was measurable value. That became our rule: if it doesn't change outcomes, it doesn't matter.

When the pandemic hit, we volunteered our skills to help analyze global COVID datasets. It reinforced something we still believe: real-world data is messy, and the systems that matter must hold up under imperfect conditions.

Then the mortgage market surged and document volume exploded. We built models and pipelines to handle giant, inconsistent mortgage packages—segmenting, classifying, extracting, and verifying information in workflows where mistakes are expensive. That work made us deeply pragmatic about what it takes to ship AI: evaluation, review gates, and ownership.

We stayed close to core technology: tuned GPT-2, trained transformer models for specialized tasks, and fine tuned document models like Donut and LayoutLM when required. We didn't just "use AI"—we learned how it breaks and how to make it reliable.

Now the world is flooded with vibe-coded agents and trendy tools. We see two gaps everywhere: most teams don't have data foundations ready for serious automation, and many build AI because it's fashionable. Our focus hasn't changed: we learn your context and constraints, engineer the right system, and ship something that delivers outcomes people can rely on.