Austin Artificial Intelligence

Robert Corwin spent 20+ years in quantitative finance and co-founded EVA Capital, a quant hedge fund trading long-short factor strategies in US equities. However, his hedge fund raised between $50-100 million—well below the $500 million to $1 billion minimum he determined was necessary for a sustainable hedge fund business after accounting for administrative costs, databases, computing infrastructure, and employee salaries. "A percent of 100 million is a million bucks," he explained, "but you're paying for a lot of databases and computers and employees are expensive in that industry."

Rather than remain stuck in that competitive, brutally difficult space, Corwin recognized that the quantitative analysis work dominating finance and defense for 20-30 years was finally reaching inflection point across all other industries. "The rest of the world is now finally catching on to data science, to quantitative analysis," he noted. With cloud computing power no longer a bottleneck—thanks to AWS and similar platforms—he saw the real opportunity: helping companies solve actual data science problems instead of selling them frameworks that promised to be panaceas.

Corwin launched Austin Artificial Intelligence officially in 2021 with a clear philosophy: most companies had spent millions on AI and ML frameworks that unified things and added abstraction, but "they don't solve your data science problems at the end of the day by themselves." His insight was that every problem required the devil-in-the-details work: good experimental design, data engineering, quality pipelines, coding, and most importantly, smart people actually running analyses.

The core offering became a packaged services model positioned between pure services and product. Austin AI would deploy a "pod"—a small team with a specific structure: a senior data scientist working part-time managing a mid-level data scientist/engineer full-time, plus two to three junior resources (a mix of engineers and data scientists) based in the US and abroad. As of the interview, the company had five full-time people dedicated to the effort in the US, roughly the same abroad, plus support from their investor Silicon Partners, bringing the total team involved to around 20 people.

They also licensed utility functions and core code libraries to clients as part of their service, handling IP discussions on a case-by-case basis—typically allowing clients to own work-for-hire material while retaining the right to work with other companies and reuse non-proprietary components.

Corwin leveraged relationships rather than traditional outreach. He knew a VC in Austin who was connected to Silicon Partners, a 400-500 person consulting firm seeking data science capabilities. After meeting the founder multiple times and building genuine rapport, Corwin structured an angel investment deal (not a traditional Series A) in 2021. The relationship was symbiotic: Austin AI became the data science unit of Silicon Partners while maintaining independence to serve external clients.

This partnership proved immediately valuable. When asked how many of his seven current customers came from Silicon Partners, Corwin confirmed it was a significant portion, demonstrating how the strategic relationship became his primary customer acquisition channel. The minimum deal size was roughly $50K-$200K for three-month-plus projects with two resources—comparable to hiring a full-time employee—though smaller deals existed for legitimate projects.

As of the interview, Austin AI was working with approximately seven customers across technology, financial, and industrial sectors. The company was profitable and operating with lean efficiency—charging significantly below market rates for hiring individual data scientists while delivering faster results through their operational structure and pod methodology.

When pressed on the future by host Nathan Latka, who noted that successful SaaS founders often start as agencies then build product once they spot repeated problems, Corwin acknowledged the pattern but remained focused on the services business. "Right now we're focused on being a services firm with some product ideas in our back pocket," he said. He was comfortable with the current trajectory: "Business is growing as it is. We're growing like a startup, a SaaS startup anyway, for the time being." He understood the eventual ceiling on services scaling but wasn't racing toward productization—the relationships, hiring pipeline, and strategic partnership with Silicon Partners were enabling rapid growth without VC pressure to chase the SaaS model.