AI for logistics and supply chain, built on the data you already have.

A demand forecasting model needs historical order or sales data, typically 18-36 months minimum, with enough granularity to detect seasonality and trend. Beyond the base demand history, the model improves significantly when you add external signals: promotional calendars (what promotions ran when), inventory availability history (was a stockout driven by demand or supply?), pricing history, and where relevant, external signals like weather data, economic indicators, or commodity prices. The model architecture we choose depends on the data you have. For most logistics and distribution businesses, a gradient boosting model or a temporal fusion transformer trained on your order history gives meaningfully better accuracy than a statistical forecast from a spreadsheet. We assess your data in discovery and tell you what accuracy improvement is realistic before we build.

Exception prediction is a classification problem. A model is trained on historical shipment data: shipments that completed on time, and shipments that experienced delays, damaged goods, missing documentation, or carrier failures. The model learns which combinations of signals, carrier, lane, origin-destination pair, time of year, weather conditions, shipment weight and dimensions, and days in transit, predict exceptions before they happen. At the point of booking or during in-transit monitoring, each shipment is scored. High-risk shipments are surfaced to your operations team with the contributing risk factors so they can intervene: hold alternatives ready, alert the customer proactively, or escalate with the carrier before the exception becomes a miss. The key difference from reactive tracking is that the alert comes before the delay is confirmed, not after.

Warehouse slotting is the assignment of SKUs to pick locations based on velocity, pick frequency, and co-order patterns. A poorly slotted warehouse has pickers traveling long distances for high-velocity items and fast-moving SKUs stored in inconvenient locations. Traditional slotting uses velocity-based rules: A, B, and C items by pick frequency. AI-based slotting adds co-order analysis, which identifies SKUs that are frequently picked together in the same order and places them near each other, and temporal patterns, which identify how velocity changes by day of week, month, or season. The output is a recommended slot assignment that reduces total pick distance and therefore pick time per order. For high-volume operations, slotting optimization typically reduces pick travel distance by 15-30%. We build this as a model that runs against your WMS data on a scheduled basis and produces re-slot recommendations, not as a one-time exercise.

Standard TMS routing solves the vehicle routing problem using rules-based optimization: minimize distance or time given a set of stops and vehicle constraints. This works well for predictable, static conditions. AI-based route optimization adds two capabilities standard TMS tools lack. First, it incorporates real-time signals: live traffic conditions, weather, road incidents, and driver performance history. It re-optimizes routes dynamically as conditions change, not just at the start of the day. Second, it learns from historical outcomes: which routes resulted in late deliveries, which drivers perform better on specific lane types, which stop sequences cause driver overtime. Over time, the model improves its routing quality because it learns from your specific operation rather than applying generic optimization rules. For fleets running 50 or more routes per day, the combination of dynamic re-optimization and learned performance patterns typically reduces fuel cost and late deliveries meaningfully. We scope the specific impact against your data during discovery.

Most logistics AI projects with RaftLabs run between $30,000 and $120,000 depending on scope. A focused document extraction system for a single document type typically sits at the lower end. A demand forecasting model covering 5,000 SKUs across 10 locations with external signal integration is a larger scope. We scope the work in a paid discovery engagement before any development starts, then give you a fixed-price quote. The price you see in week 1 is the price on the final invoice, unless you change the scope.

Voice-directed picking delivers task instructions through a headset and takes verbal confirmation before advancing to the next task, so workers stay mobile instead of stopping to read a screen or a paper list. This eliminates the misread-screen and illegible-paper-list errors that drive most pick mistakes. Warehouses transitioning from scan-based picking typically see error rates drop 20 to 40 percent within 90 days. Deepgram's noise-tuned transcription models are essential here, general-purpose speech recognition performs poorly against conveyor noise, forklift traffic, and PA announcements, while warehouse-tuned models hold word error rates below 3 percent in the same conditions.

Yes. We sign mutual NDAs before any scoping conversation where you share operational data, pricing data, or proprietary processes. For enterprise logistics clients, we are open to signing your standard NDA form rather than asking you to use ours. Confidentiality extends to your data: model training data is used only to train your model and is not shared across client accounts or used in any way outside your project.