Results

Real outcomes from real AI engagements.

A selection of anonymized case studies from work across SMB and enterprise AI builds. Client names will be added where allowed.

448%

Engagement lift on an AI-driven product experience

Problem: A core product surface was underperforming on engagement. Users weren't coming back to the feature that was supposed to be the hook.
Approach: Reworked the experience around an AI-driven layer that personalized what users saw, when they saw it, and why it mattered to them - pairing model behavior with UX patterns the team could measure and iterate on.
Result: Engagement on the surface lifted 448%, turning a struggling feature into one of the product's strongest retention drivers.

200%

Problem: AI initiatives were taking too long to ship. By the time something launched, the market had moved and internal momentum was gone.
Approach: Restructured how the portfolio approached scoping, validation, and handoffs - borrowing from SaaS product playbooks honed across billions in ARR - so AI projects had the same rigor and pace as any other product bet.
Result: Time-to-market across the AI portfolio improved by 200%, with no drop in quality or adoption.