From Manual Surveys to AI-Assisted Intelligence: Our Journey

Before adopting AI, our teams manually surveyed pavement distress using measuring wheels — a time-consuming, labor-intensive, and hazardous process that put staff dangerously close to fast-moving traffic. This approach demanded extensive personnel and resources while creating unacceptable safety risks.
Today, we've pioneered a hybrid pavement condition survey approach that strategically fuses manual expertise with AI capabilities. Rather than jumping to full automation, we use AI as a force multiplier: it identifies distresses our teams might miss and accelerates the measurement process, allowing our professionals to focus on judgment rather than tedious data collection.

Why We're Not Fully Automated (And That's Intentional)
Our models are technically ready for full deployment, but we're not rushing there. Here's why: AI should assist professionals in making informed decisions, not replace professional judgment entirely. While our technology handles detection and measurement, our certified raters validate results, account for context, and make the final calls.

This deliberate approach has allowed us to refine our models over six years, training them on hundreds of thousands of real-world images. Most recently, we've integrated LiDAR with our imaging systems, enabling our AI to assess pavement depth — something nearly impossible with 2D images alone. When we do transition to greater automation, accuracy won't be a question mark; it will be a proven fact.

Built In-House, Without Outside Influence
AMS doesn't have private investors backing our technology development, and we don't partner with third-party AI vendors. We've built our solution from the ground up, ensuring it addresses real pavement challenges rather than investor expectations or vendor limitations. This independence means our technology evolves based on what actually works in the field, not what sells in a boardroom.

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