AI Development for
Logistics
Route optimisation, demand forecasting, fleet intelligence, and warehouse AI for logistics teams. Real production experience. India based engineering team.
Logistics runs on milliseconds and margins. A route that is 10 percent more efficient, a demand forecast that is 15 percent more accurate, or a maintenance alert that catches a vehicle failure before it happens: these are not incremental improvements. They compound across thousands of routes and vehicles every day.
We have built AI-powered logistics products that operate in the real world, not just in demos. Here is what we do.
What We Build for Logistics Teams
Route Optimisation
ML models that optimise delivery routes in real time, accounting for traffic, vehicle capacity, delivery windows, and driver availability. Cuts fuel costs and on-time delivery failures at the same time.
Demand Forecasting
Time series models that predict shipment volumes by lane, region, and product category. Helps operations teams staff correctly, allocate capacity, and avoid penalty costs from underestimating demand.
Fleet Intelligence and Tracking
Real-time GPS tracking, driver behaviour analysis, and route adherence monitoring. Surfaces operational insights that fleet managers can act on, not just raw telemetry data.
Warehouse and Inventory AI
Picking optimisation, slotting recommendations, and inbound volume prediction for warehouse operations. Reduces pick walk time and improves throughput without adding headcount.
Predictive Maintenance
ML models trained on vehicle telemetry data that predict component failures before they happen. Shifts maintenance from reactive and expensive to scheduled and manageable.
ETA and Visibility AI
Dynamic ETA prediction that accounts for historical delays, traffic patterns, and real-time conditions. Gives customers accurate delivery windows and reduces inbound support queries.
TranziGo: A Mobility as a Service platform integrating 40 plus transport providers
We built TranziGo end to end for iOS and Android: a unified mobility platform that connects buses, trains, bikes, scooters, and ride-sharing in one app with real-time GPS, AI route suggestions, and in-app payments. The platform launched with 40 plus transport providers integrated and received a 4.6 star App Store rating from early users.
Read the TranziGo Case StudyWhat makes logistics AI different from other enterprise AI
Logistics AI has to work in the real world, not in a controlled environment. Routes change because of accidents. Vehicles break down. Demand spikes unexpectedly. A model that performs well on clean historical data but degrades under real-world noise is worse than no model at all, because it creates false confidence. We build logistics AI with operational resilience as a design requirement from the start. That means fallback logic, anomaly detection, and human-in-the-loop override mechanisms are part of the system before it goes to production, not added later when something breaks.
Logistics products we are most experienced building
- Last-mile delivery route optimisation systems
- Multi-modal transport and urban mobility platforms
- Fleet tracking and driver performance management tools
- Warehouse management and picking optimisation systems
- Demand forecasting for capacity and resource planning
- Predictive maintenance for vehicle and equipment fleets
Building AI for logistics or supply chain?
Operations AI needs to be reliable in the field, not just in the demo. Tell us what you are building and we will tell you how we would approach it.
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A118, Sector 63
Noida, UP 201301
304 Krishna Classic, A.B Road
Indore, MP 452008