Civil · Infrastructure AI

AI for Civil Engineering

Our civil practice is built on pavement and infrastructure work for agencies and engineering teams. We build the decision-support software, and your licensed engineers own the analysis and every call.

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Two overlapping screens showing pavement condition and street infrastructure data analysis; the back screen displays a dark-themed performance summary report with metrics and charts for smart street SR-23, and the front screen shows a city map with color-coded street segments representing pavement condition index and pipe replacement data for the City of Elmdge, CA.

4Platforms in use

2Analyses modernized: runway and highway

1 → 3Same fieldwork becomes a report, a dashboard, and a plan

0Changes to the math reviewers already trust

Composite dashboard displaying multiple maps and charts for roadway pavement intelligence and street condition monitoring in California, including PCI, IRI, rutting, and friction metrics, section details, core recordings, heatmaps, pass/fail by route bar charts, route statistics tables, and performance visualization for pavement tests. — Pavement condition dashboards and maps

Project Overview

We build decision-support software for pavement and infrastructure teams: tools that turn the same fieldwork into a report, a dashboard, and a plan. The goal is to let agencies and firms compete on the decision rather than the data collection, and to automate the manual middle that eats a team's hours.

Our Role

We handle product design and the full-stack build. A few of the systems we've built:

A spatially optimized capital-improvement planner that ingests standard pavement-management exports, runs budget-constrained multi-year analysis across ten prioritization strategies with deterioration modeling, then clusters funded projects for efficient contract packaging and outputs a full agency deliverable set: report, executive summary, Excel work plan, and GIS package
A secure pavement-performance dashboard for a major toll-highway network: network KPIs, an interactive web map with dynamic filtering, a searchable section browser, and capital-planning views charting cost against budget, all behind managed authentication
A pavement-marking retroreflectivity platform: mobile data synced to GPS survey video, a pass/fail dashboard, a threshold-based GIS map, a video player with a live data overlay, one-click export to Excel, shapefile, and KML, and a re-striping planner that finds deficient corridors and estimates cost
A modernization of a legacy pavement-evaluation engine for runway and highway analysis: we ported the layered-elastic, damage, and backcalculation models to Python with verified numerical parity, then wrapped them in a modern, GIS-native stack

On the legacy engine, the delivery improved without changing the math the reviewers already trust.

Impact & Results

This is decision-support, not stamped engineering. Scope and accountability stay clear, and engineering judgment stays with your licensed engineers. One of these platforms was funded and deployed by the client it was built for and runs today behind managed authentication; others are in active internal use or in development. Every one of them is built to survive a licensure, liability, and public-records review, because that is the standard the work has to meet.

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