20% Lower Fleet Costs Through Intelligent Dispatching

Logistics companies struggle with fragmented systems: TMS, ERP, and telematics deliver data in silos while dispatchers plan routes manually. According to Gartner, 85% of logistics companies plan AI investments for routing and cost prediction within the next 24 months — yet the reality shows that deadheading, idle time, and inaccurate demand forecasts cost millions in avoidable expenses annually. Meanwhile, fuel prices keep rising alongside mounting pressure for CO₂ reduction.

Data Integration

The platform connects TMS, ERP, and telematics systems through standardized APIs into a unified data layer. Vehicle positions, order data, and historical delivery patterns converge in real time — no manual exports or spreadsheets required. Supabase serves as the central data hub with row-level security for multi-tenant operations.

AI Model & Optimization Logic

A hybrid system combines mathematical optimization with natural language processing. The optimization engine calculates cost-minimal route plans factoring in time windows, vehicle capacities, and driver qualifications. The language component lets dispatchers issue instructions via chat: "Prioritize express deliveries in Hamburg" is automatically translated into planning parameters. TensorFlow-based demand forecasting anticipates order spikes up to 14 days in advance.

Deployment & Monitoring

The Next.js dashboard visualizes fleet utilization, cost trends, and KPI deviations in real time. When disruptions occur — traffic, vehicle breakdowns, order changes — the system automatically simulates alternative scenarios and recommends the best replan. Structured alerts escalate critical deviations to the responsible team members.