AI Trucking Optimization Platform

I’m kicking off the build of an AI-powered platform that takes the headache out of trucking operations by unifying route optimization, load matching, and real-time dispatch automation. The first milestone is an investor-ready MVP, so every line of code must demonstrate clear ROI and scalability.

Here is the ground we need to cover
• Core logic: a Python backend (FastAPI or Django) that exposes clean REST/GraphQL endpoints for vehicle-routing, load/tractor pairing, and automated dispatch decisions. OR-Tools, linear programming, or any proven heuristics are fair game—as long as we hit near-real-time results on typical fleet sizes.
• User flows: drivers, dispatchers, and fleet managers will each log in to see only what matters to them—think mobile-first for drivers, dashboard for ops, and fleet-wide KPIs for management.
• Integrations: out-of-the-box hooks for GPS/Tracking systems, Order Management Systems, and telematics devices. If an integration kit already exists, great; if not, we’ll define a clear adapter pattern.
• Architecture: I’m leaning toward a micro-service core (optimization engine, event bus, auth, and UI) running on AWS or GCP, but I’m open if you have a better approach that accelerates time-to-market without throttling future scale.

What I value most is hands-on experience delivering optimization engines—beyond dashboards—and a thoughtful plan for an MVP that investors can demo. If you’ve shipped a vehicle-routing or fleet-management tool before, let’s talk specifics, share past repos or case studies, and outline a phased roadmap with measurable milestones.