Real impact. Measurable results.
We don't do testimonials without the numbers. Here are the actual outcomes from actual deployments — with the client, the challenge, and the result.
Global Investment Bank
A top-5 global investment bank was experiencing escalating payment fraud losses with their legacy rule-based system generating too many false positives — blocking legitimate customers — while still missing novel fraud patterns.
"The fraud savings paid for the engagement in month one."
— Head of AI & Risk Technology
Fortune 50 Retailer
A Fortune 50 retailer with 4,200 stores was carrying $1.2B in excess inventory while still experiencing 28% stockout rates on high-velocity SKUs. Their 5-year-old statistical forecasting model couldn't account for the complexity of modern demand signals.
"The seasonal accuracy jump from 68% to 94% in one quarter changed how we think about planning."
— Chief Supply Chain Officer
National Health System
A national health system with 52 hospitals was struggling with a 19% 30-day readmission rate — above the national average and triggering CMS penalties. Their care teams had no systematic way to identify which patients needed more intensive post-discharge support.
"This is the first AI model our clinicians actually trust and use every day."
— VP of Clinical Analytics
Top-10 SaaS Platform
A top-10 SaaS platform's 6-year-old recommendation and ad targeting system was built on collaborative filtering with manual feature engineering. CTR had plateaued and advertiser churn was accelerating.
"6 weeks from kickoff to 180M users. That kind of execution is rare."
— VP Engineering
Global Automotive Manufacturer
A top-5 global auto manufacturer was experiencing $150M in annual unplanned downtime across 14 plants. Their maintenance team operated reactively, and their sensor data sat unused in siloed historians.
"We went from reacting to failures to preventing them. That's a fundamental shift in how we operate."
— VP of Manufacturing Operations
Fortune 100 Insurer
A top US property & casualty insurer was losing commercial clients to faster competitors. Their underwriting process took 3–5 days and relied on manually-assembled data packs — while their loss ratios were deteriorating.
"We can now compete on speed without compromising on risk quality."
— Chief Underwriting Officer
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