About ATC
ATC was built by the team behind two decades of technology-ecosystem research for institutional investors. Today we apply that discipline to a different decision: where a software company can win, why its product matters to a specific prospect, and who needs to hear the story.
The throughline
Research discipline
Understand the product
Capabilities, positioning, dependencies, proofUnderstand the account
Stack, initiatives, problems, peopleMake the connection
Fit, technical wedge, buying path, messageWhy we're different
Our founders built their careers finding the relationships other people missed: how technologies interact, where adoption is moving, and which weak signals add up to conviction. ATC brings that rigor to account intelligence for software teams.
Built a leading technology-focused investment bank whose ecosystem research was relied on by the largest institutional technology investors.
Pioneered an approach that mapped how technologies interact and evolve — not merely what each product does in isolation.
Brought investor-grade research discipline to GTM: the product intelligence and account context AI needs to produce a defensible plan.
The team
ATC's leadership first worked together across technology research, data, and investment banking. Today that shared operating history powers one integrated product, research, and customer team.
CEO
Scott sets ATC's vision, strategy, and priorities, continuing the role he held as Chairman and CEO of Pacific Crest Securities.
President
Steve leads project development, customer success, and daily operations. He previously served as Director of Research at Pacific Crest Securities.
CTO
Ben leads product strategy and research workflows. At Pacific Crest Securities, he led the analysis and integration of data into the firm's investment research.
CPO
Daniel leads AI, data science, and application engineering. He previously served as Chief Data Scientist and CTO at Pacific Epoch, Pacific Crest Securities' China research team.
How we work
The technology changed. The standard of evidence did not.
A technology only makes sense in context: what it depends on, what it replaces, how it is adopted, and which initiatives pull it forward.
Useful account intelligence starts with a deep model of the software being sold and the problems it can credibly solve.
Every priority, wedge, person, and message should trace back to account evidence — not a fluent model's best guess.
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