Supply Chain Optimization

The promise of an "AI-optimized supply chain" generally arrives as a single product trying to be every layer of your operation at the same time. The version of this work that actually holds up tends to be narrower. A few well-scoped models, plugged into the systems you already operate, doing specific jobs better than the heuristics they replaced.

Where it lives in your stack

• Demand forecasting. Better than the spreadsheet, in cases where you have enough history to support the model.
• Inventory placement. Where to hold what, taking lead times, demand variance, and storage cost into account.
• Routing and load planning. Optimization in the cases where the constraints are tractable and the savings genuinely outweigh the added complexity.
• Anomaly and exception management. Catching the late shipment, the unusual order, or the supplier going dark, before any of them become a Monday-morning emergency.
• Document automation. Bills of lading, customs forms, and supplier invoices, extracted into your systems automatically.

Where we'll be honest with you

Some supply chain problems aren't especially data-tractable. Geopolitical disruption isn't something a model is going to predict for you. Black-swan demand isn't in your training data, more or less by definition. The job of AI here is to do the foreseeable parts better, so your team can spend more of their attention on the parts that aren't.

How a deployment works

The system gets configured around your specific ERP, WMS, TMS, and supplier systems. We operate it. We monitor it. We retrain on a schedule. Your operators don't have to learn a new tool, because the AI shows up inside the systems they're already using all day. Get in touch.