AI-Powered Order Operations at Scale

The Challenge

A fast-growing direct-to-consumer brand had built their operations on a patchwork of manual processes and disconnected tools. At 500 orders per day, their team of 12 operations staff could barely keep up. When a viral product launch pushed volume to 8,000 orders in a single day, the system collapsed — orders were lost, customers were furious, and the team worked around the clock for a week to recover.

The root problem wasn't volume — it was complexity. Each order required decisions: which warehouse to fulfill from, how to handle split shipments, when to flag for fraud review, how to route international orders through customs. These decisions lived in the heads of experienced staff, encoded nowhere, impossible to scale.

The company had tried off-the-shelf OMS solutions, but none could handle their specific product catalog complexity — bundles, subscriptions, pre-orders, and custom configurations all in the same order. They needed something built for their business, not adapted from a generic template.

The Solution

We built an AI-native order operations platform from the ground up. The core was a decision engine that encoded the team's institutional knowledge into a set of ML models and rule graphs — not replacing human judgment, but making it infinitely scalable. Every routing decision, every fraud flag, every exception handling path was captured, tested, and automated.

The fraud detection layer used a gradient boosting model trained on 18 months of historical order data, with real-time feature engineering from behavioral signals, device fingerprinting, and network graph analysis. It reduced chargebacks by 71% while approving 99.2% of legitimate orders automatically — the remaining 0.8% were routed to a human review queue with full context pre-populated.

For fulfillment routing, we built a multi-objective optimizer that balanced shipping cost, delivery speed, and warehouse capacity in real time. The system integrated with all three of their 3PL partners via a unified API layer, with automatic failover when a warehouse was at capacity or experiencing delays. International orders were handled by a dedicated compliance module that pre-calculated duties, generated customs documentation, and selected the optimal carrier for each destination country.

The entire platform was built on an event-driven architecture using Kafka, with each order moving through a series of processing stages as a stream of events. This made the system naturally resilient — any stage could fail and retry without losing order state, and the team could replay historical events to debug issues or test new logic.

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