Get paid for your expertise.

We started Sophia with a simple observation: the most valuable knowledge in the world lives inside people's heads, and until now most of it never left.

Think about what it takes to become genuinely excellent at something. The surgeon who reads a scan the way others read a page. The engineer who knows a system won't scale without testing it. The M&A attorney who spots the clause that will kill a deal in diligence. None of that came from a textbook. It came from decades of practice, of failures nobody writes down and judgement nobody teaches. Real expertise lives in experience.

That expertise has always been scarce, bounded by a single person, a single career, a single lifetime. The best surgeon in the world can only stand in one operating room. Knowledge accumulates inside individuals for decades and mostly stays there.

For the first time, that can change. By capturing the patterns, decisions and reasoning of the best practitioners, models can carry that knowledge further than any individual could. This is what Sophia is building toward: a world where an insight that took a lifetime to form is no longer limited to a lifetime's reach.

Data quality is the missing layer for professional AI

The distance between the world today and the one we're building comes down to data quality. Today's models ace the SAT and write entire dissertations. Put them in front of the workflows professionals run every day and they fall short.

Tomorrow's models won't just autocomplete sentences. They'll help structure billion-dollar transactions, design personalised treatment plans, and read case law in minutes that would take a person hundreds of hours.

Getting there takes training data and evaluation environments that reflect how professionals actually work: the edge cases and judgement calls that can't be scraped from the web or synthesised from first principles. It's the layer the field has underinvested in, and the one Sophia is being built to fill.

Research-driven, software-first

We start with research: where exactly do models break down in real professional contexts, and why? Every domain has its own failure patterns, and the goalposts move as the work evolves. This isn't a problem you solve once.

From there we take a software-first approach to data creation. Rather than outsourcing collection, we build the tools and workflows in-house for every project. That means better data, closer oversight, and a better experience for the experts who contribute.

Join us

Our team comes from finance, software engineering, consulting, quant trading and research, and we're here because advancing frontier models is one of the highest-leverage problems of our generation. The data layer is the first step.

We're hiring across operations, engineering and research as we onboard our first experts. Wisdom that took a lifetime to build shouldn't take a lifetime to find. If you want to work on a generational problem alongside people who care about it as much as you do, we'd love to talk.