AI Consulting for
Logistics & Supply Chain

For most operators, route optimization and demand forecasting pay back first. Route optimization that accounts for live traffic, delivery windows, and vehicle capacity typically cuts fuel and mileage 15-25% and lifts on-time delivery — and because it plugs into existing dispatch, it can be live in 3-5 weeks. Demand forecasting at the SKU/location level reduces both stockouts and excess inventory, freeing working capital quickly. Predictive fleet maintenance and warehouse computer vision deliver strong returns too, but usually need more sensor or historical data before they reach full accuracy.

Agility ships production logistics AI in 3-8 weeks depending on data readiness and integration scope. A route optimization or ETA-prediction system that reads from your existing TMS/order data takes about 3-5 weeks. A demand-forecasting model needs 12-24 months of historical order and inventory data and typically takes 4-6 weeks including data engineering. Predictive fleet maintenance depends on how much telematics/IoT history is available — with a clean sensor feed it lands in 5-7 weeks. We work in weekly sprints and validate against your real lanes and SKUs, not synthetic demos.

For demand forecasting, we need historical order or shipment volumes by SKU and location (ideally 12-24 months), plus any known drivers like promotions, seasonality, and pricing. For route optimization, we need stop/delivery data, vehicle capacities and constraints, service windows, and — where available — historical GPS or telematics traces. Live inputs such as traffic and weather are integrated via API. We handle the ETL and feature engineering, and can work with data from most TMS, WMS, and ERP systems or flat-file exports.

Yes. We integrate with transportation management, warehouse management, and ERP systems via REST APIs, event streams, or database connectors, and with telematics platforms for fleet data. AI scoring — an optimized route, an ETA, a maintenance alert, a replenishment trigger — is delivered back into the systems your teams already use, so dispatchers and planners do not have to learn a new tool. For legacy systems without modern APIs, we use file-based or RPA integration. Integration design is scoped up front and included in the delivery timeline.

Yes — the economics often work better for mid-sized operators because a few points of fuel, capacity, or inventory efficiency move the P&L meaningfully. We scope to the single workflow with the clearest return first (commonly route optimization or forecasting), prove it on your data, and expand from there. Fixed-scope pricing and a 3-8 week timeline keep the initial commitment contained, so you see measurable results before investing further.

Accuracy depends on data quality, but well-built models are consistently better than manual or rules-based baselines. Demand forecasts commonly reach 90%+ accuracy at a 30-day horizon for stable SKUs, with wider bands for new or intermittent items. AI ETAs typically land within a ±2 hour window for line-haul and tighter for last-mile once live traffic is integrated. We report accuracy honestly against your historical baseline during a validation phase before go-live, and include 90-day monitoring so performance is tracked as conditions change.

Each project is scoped to a fixed price after a free assessment, based on the use case, the state of your data, and integration effort. A focused route-optimization or forecasting build is smaller than a multi-model program spanning fleet, warehouse, and pricing. You approve scope, milestones, and price before any build begins — no open-ended retainers — and production monitoring is included as standard.