I. Executive Summary

This case study outlines a groundbreaking AI-powered platform designed to modernize the road maintenance industry. By tackling two of the sector's biggest challenges—quality control and equipment management—the solution transforms traditional, reactive operations into a proactive, data-driven, and highly efficient process. The platform consists of two core modules: an AI vision system for real-time quality control of road marking application, and an AI analytics engine for predictive monitoring of maintenance machinery. This dual approach provides an end-to-end solution that reduces costs, ensures compliance, and delivers a significant competitive advantage in a market lagging in technological adoption.

II. The Challenge: The High Cost of Inefficiency in Road Maintenance

The road maintenance industry, particularly in Poland and across Europe, often operates on legacy processes. Quality control for critical tasks like road marking is typically manual, subjective, and performed after the fact, relying on selective spot-checks. This reactive approach leads to disputes, costly re-work, and wasted materials. Simultaneously, while modern machinery is equipped with sensors, the data they produce is rarely analyzed intelligently. Fleet managers are left with raw data logs, unable to proactively identify operational inefficiencies, predict equipment failures, or objectively measure crew performance against technical specifications. This lack of intelligent oversight results in unplanned downtime, excessive material consumption, and a lack of transparency for both the contractor and the client.

III. The Solution: A Dual-Module AI Platform for End-to-End Control

To address these challenges, a comprehensive AI solution was developed, comprising two integrated modules that can be deployed on any road maintenance vehicle.

Module 1: AI Vision for Real-Time Road Marking Quality Control

This module acts as a digital inspector, ensuring quality is built-in, not just inspected later.

Technology: The system uses high-resolution industrial smart cameras mounted on the vehicle, coupled with a GPS module for precise geo-tagging.

AI at Work: A sophisticated Computer Vision (CV) model, trained on thousands of examples, analyzes the freshly applied road markings in real time. It instantly detects any deviations from GOST/EN standards, including:

- Incorrect line width, geometry, or shape. - Breaks, smudges, or gaps in the marking. - Deviations from the planned trajectory.

Outcome: This shifts quality control from a delayed, manual process to an automated, in-process guarantee. Issues are flagged instantly, allowing for immediate correction and generating a transparent, evidence-backed report for project acceptance.

Module 2: AI Analytics for Predictive Machine Monitoring

This module serves as the fleet's digital brain, turning raw data into actionable intelligence.

Technology: The AI engine integrates with the vehicle's existing monitoring systems, analyzing parameters like speed, pressure, and material flow rates.

AI at Work: The system compares real-time operational data against the normative values set in the project's technical specifications. It automatically identifies and flags anomalies that are impossible to spot manually, providing insights through:

- A Centralized Dashboard: Offering a real-time overview of all machines, active alerts, and performance KPIs. - Automated Reports: Detailing performance metrics, deviations from protocol, and predictive maintenance forecasts. - Instant Alerts: Notifying managers of critical events, such as deviations from technical parameters or an impending equipment failure.

Outcome: This enables a shift from reactive repairs to predictive maintenance, optimizes the use of expensive materials like paint and thermoplastic, and provides objective data for evaluating crew performance.

IV. The Business Impact: A New Standard in Efficiency and Competitiveness

The implementation of this AI platform delivers tangible benefits across the entire operation, establishing a new benchmark for the industry.

V. Implementation and Investment

The solution is designed for flexible and scalable deployment. A pilot project for the AI quality control module on several machines can be implemented in approximately 3 months for an investment of €40,000-€50,000. The AI monitoring module starts from €50,000 with a timeline of 4+ months, depending on the complexity of reports and integrations.

VI. Conclusion: Paving the Way for the Future of Road Maintenance

This AI-powered platform represents a paradigm shift for the road maintenance sector. It replaces outdated, inefficient, and opaque manual processes with an intelligent, automated, and transparent system that delivers superior quality at a lower cost. By providing real-time quality control and predictive operational insights, the solution empowers contractors to manage their projects with unprecedented precision, reduce financial risk, and solidify their position as technological leaders in the industry.