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.

50%

Voice agent cut an SMB's inbound calls to product specialists in half

Problem: Product specialists were spending hours each day on routine inbound calls — questions that didn't require their expertise but blocked them from higher-value work.
Approach: Designed and shipped a voice agent that handled the routine calls end-to-end, with a clean handoff to a human when escalation was warranted. Built with product and UX rigor so callers got fast, accurate answers — not a phone tree that made them angrier.
Result: Inbound calls to product specialists dropped by 50%, freeing them to focus on the conversations that actually moved revenue.

448%

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.

Next step

Tell me what's eating your team's time. I'll come ready with a point of view on what's possible — not a sales pitch.