Real results. Measured, not estimated.
Every number on this page comes from a shipped product, a post-launch measurement, or a board-level business outcome. No projections.
Aggregate outcomes across all engagements
The outcomes that mattered most
Two of three pilot enterprise customers on an oil & gas AI platform were at churn risk - not from model accuracy, but from missing explainability. Redesigning the confidence layer and evidence panel converted both to annual contracts.
The AI decision platform surfaced raw probability maps experts wouldn't act on. Redesigning around confidence intervals, interrogable suggestions, and structured overrides cut interpretation cycles from 6.5h to 1.8h and deepened expert sessions from 12 to 47 minutes - and the structured-override dataset it produced was later cited as a proprietary moat in the Series C raise.
Six enterprise products carried divergent components with no shared architecture. A 4-layer token system unified all six across three client brands with zero forks - cutting feature-delivery time 40%.
A Gulf petrochemical manufacturer ran procurement over email and Excel - $2M+ in annual waste, no audit trail. The 0→1 AI-enabled platform cut approval cycles from 11 to 4 days and made every transaction auditable from day one.
Corporate banking customers were visiting branches for tasks the app should have handled. Rebuilding nine task paths into four primary journeys - and card-freeze from four screens to two taps - drove 94% task completion and a 23% drop in branch visits within 90 days.
What changed, and by how much
State of each system before I joined versus post-launch reality. Not design intent - shipped outcomes.
- 6.5hr interpretation cycles per session
- 5+ disconnected legacy tools
- Raw ML outputs with no context
- 12 min average session depth
- 2/3 pilot customers at churn risk
- 1.8hr cycles - 72% reduction
- Single interpretation canvas
- Confidence ribbons + evidence panels
- 47 min average session depth
- 2/3 pilots converted to annual contracts
- 6 products with no shared components
- 3 client brands built as separate forks
- No token system or naming conventions
- 12+ day average feature delivery
- Design debt compounding with every sprint
- 87 shared components from 1 system
- 3 brand themes via token overrides
- 4-layer token architecture
- 7 day average feature delivery
- Zero component divergence across teams
- 11-day approval cycles via email
- Excel-based tracking, no audit trail
- Zero supply chain visibility
- $2M+ annual operational waste
- Manual reporting - 3 days per cycle
- 4-day cycles - 63% reduction
- Real-time approval tracking
- Full supply chain intelligence layer
- 100% audit compliance from day 1
- Live reporting - 3 days → real-time
- 9 unstructured task paths
- Card freeze required 4 screens
- High branch dependency for digital tasks
- High support call volume
- Users describing app as 'confusing'
- 4 clear primary journeys
- Card freeze in 2 taps
- 94% task completion in testing
- −41% support call volume
- −23% branch visits within 90 days