We don’t share names. We share numbers.

Payment processing errors show up slow. A store sees declines spike, operations teams notice hours or days later, and by the time leadership triages the root cause — internal system, card network, BIN-level issue — millions in revenue have already walked. Manual detection across 2,000+ stores is impossible, and without attribution the fix lags the loss by days.

Diwo ships Catalyst-powered anomaly models across every store, every card type, every BIN. The platform predicts processor failures six hours in advance, detects real-time anomalies inside the prediction window, and separates internal system issues from external vendor problems so the right team gets paged. Payment Operations and Store Operations move from post-hoc reporting to proactive, attribution-backed escalation.

Warehouse hubs staff to yesterday's averages and then react to today's reality. Package flow inefficiencies compound quickly — a downstream station runs hot while pickers stand idle three aisles over. With no predictions at the hub level and no visibility into expected volumes, supervisors are always one shift behind. The result is lost revenue and unused capacity, every single shift.

Diwo's Decide platform gives Hub Control Tower Supervisors real-time resource-allocation recommendations — anticipating needs and dynamically updating who should be where. Pre-shift plans and in-shift reassignments both get quantified recommendations, backed by projected throughput and shift-target attainment. The supervisor sees the trade-off before they commit, and a single reallocation can be worth millions across a high-volume hub.

The top pain point in enterprise AI/ML isn't model accuracy — it's adoption. Data-science teams ship recommendations that operators don't consume, with no feedback loop, no evidence, and no visibility into what's missing. Harvest Operations leadership couldn't tell whether a recommendation was ignored because it was wrong, because it lacked context, or because the business variable that mattered wasn't in the model.

Diwo turns model output into interactive decisions instead of static scores. Hindsight Analysis, Integrated User Feedback, and Evidence surfaces plug the adoption gap — operators see the reasoning, flag what's missing, and the platform learns. Harvest-optimization recommendations carry their own justification, consume real-world feedback, and compound over time.

The Test & Learn team was serving 400+ issuing banks with quarterly performance-metric PDFs — human-intensive, slow to reflect life events, and built on hardcoded business rules. Issuers needed to know how life events (relocations, marriages, new jobs) were reshaping their cardholders' spending *this month* — and the quarterly cadence was leaving material revenue on the table.

Diwo replaced the manual PDF cycle with a live decision-intelligence deployment. The platform ingests issuer-level transaction signals, pattern-matches against life-event cohorts, and surfaces ranked campaign recommendations — with post-event vs. control measurement built in. Issuers now see campaign effectiveness continuously instead of quarterly, and the Test & Learn team scales personalized marketing insight across the entire portfolio.