Trust Intelligence Infrastructure

A high-volume ecommerce platform was losing revenue on both sides of the fraud problem — real fraud slipping through, and legitimate customers being blocked. The fraud model accuracy looked acceptable. The feature pipeline was not. Transactions were scored in isolation, with no buyer history, no regional calibration, and no age-segment context.

Headline results: → False positive rate reduced from 28% to 8% — 20 percentage points fewer real customers blocked → 500K transactions monitored per day across buyer profiles, regions, and age segments → Fraud catch rate maintained — accuracy held while false positives dropped

We rebuilt the scoring pipeline around behavioral signals — purchase history sequences, buyer profiles, regional risk patterns, and demographic context. Risk thresholds were recalibrated against actual business loss, not model accuracy alone.

A retail ecommerce platform was spending more to acquire each customer while recommendation quality stayed flat. The acquisition model was optimized. The behavioral data feeding it was not. 10 million customer conversations were being discarded as support overhead — none of it processed as intent signal, none of it reaching the recommendation layer.

Headline results: → 34% reduction in customer acquisition cost — recommendation-driven, no ad spend change → 18.7% churn reduction — retention improved as recommendation quality increased → 10M conversations processed into structured behavioral signals

We built a conversational signal pipeline that extracted product affinity, purchase intent, and preference context from raw conversation data. The recommendation engine was rebuilt on behavioral signals rather than product metadata alone. CAC dropped without changing a single ad.

A gaming platform’s member-get-member program was showing record signups. The numbers looked like growth. They were not. Malicious actors were generating fake accounts at scale to extract referral rewards — manufacturing user metrics without ever playing. The fraud was undetected because the growth dashboard had no behavioral validation layer.

Headline results: → 96% fake player detection rate across 1M events per day → Referral abuse stopped — reward payouts reclaimed, program economics restored → Growth metrics recalibrated to real user behavior for the first time

We built a behavioral signal pipeline across 1M daily events — session consistency, device fingerprinting, and referral graph anomaly detection. The detection model was built on engagement patterns, not just registration metadata.