Founded in late 2023, Moth Labs emerged as large language models were rapidly moving into real-world workflows—often faster than organizations could establish governance, reliability, or operational clarity. Enthusiasm for productivity gains was high, but consistent, verifiable evidence of durable impact was uneven.

​

In that environment, our methods evolved—but not our core ideas.

 

From the outset, we believed that prior state-of-the-art machine learning remained highly valuable when applied deliberately (Lamina), and that meaningful AI systems would need to be robust, fault-tolerant, and verifiable to operate safely inside real organizations (Pamela).

 

We initially approached the work with a conventional, product-led assumption: define the solution, build an MVP, iterate toward scale. Through hands-on project work—often in service-led, capital-constrained contexts—we learned where that assumption breaks down. Early certainty is rare. Full product bets are expensive. When investment dollars want in-market certainty many of the most important questions cannot be answered by roadmaps alone.

​

Across projects, a consistent pattern emerged, learning speed matters more than product polish.

​​

Today, the Lab centers on fast, disciplined prototyping in service of customer discovery. We build small, functional systems to test assumptions, surface constraints, and learn directly from real use—not intent or abstraction. These prototypes are not demos or MVPs; they are tools for inquiry.

​

Used directly with customers, small and well-aimed prototypes consistently outperform larger, speculative product efforts. They expose constraints earlier, reduce irreversible commitments, and ground decisions in observed behavior.

​

This insight now shapes how the Lab operates—and how Studio work takes form.

​

December, 2025