ContiRoute AI  (Proposed UK Venture) is building regulation-native logistics decision infrastructure for micro-fleets, owner-drivers, and SME logistics operators working in highly regulated urban environments.

Our mission

To help everyday logistics operators plan routes that are compliance-valid and economically optimal, so regulatory complexity does not reduce service reliability or profitability.

What we do

In cities like London, rules such as CAZ/ULEZ charging, congestion charging, borough access restrictions, emissions classifications, and delivery time windows don’t sit outside operations — they determine whether a job is feasible, what it costs, and whether it remains profitable. Many SMEs still handle these constraints through manual interpretation and “experience-based” decision-making, which leads to avoidable charges, delays, and margin loss.

ContiRoute AI embeds these rules directly into routing and dispatch decisions. Our approach — Regulation-Constrained Cost Minimisation (RCCM) — combines regulatory datasets with real-world operating conditions (jobs, timing constraints, traffic signals, and cost drivers) to produce routes that remain valid under changing local rules.

What operators gain
• Reduced charge exposure and fewer compliance-related cost events
• Improved on-time performance under time windows and access constraints
• Better route profitability through clear cost visibility (time, fuel/energy, charges, delay-risk proxy)
• Auditability through transparent “why this route” reasoning and dataset versioning

Why now

This is now achievable because regulatory information is increasingly digitised and accessible, and operational data (traffic, routing signals, job constraints, and cost drivers) can be processed at scale. That combination makes it practical to run regulation-aware optimisation for SMEs — not just large fleets with dedicated compliance teams.

London-first, built to scale

We are starting in London, where regulatory exposure has become a daily operational cost. Our long-term goal is to support regulated cities globally, using the same regulation-native decision model wherever local rules directly shape logistics performance.