Rather than reviewing documents, schedules, or other individual aspects of a project, Enlaye, which was incubated at Harvard Innovation Labs, analyzes data across all aspects of a project and categorizes and predicts risks such as delays or construction errors using both customer data and a matrix of similar past projects.

"Enlaye can already connect to your project data sources, whether that's SharePoint—in the majority of cases it's still SharePoint—something like Autodesk Forma [formerly Construction Cloud], or other integrations that we have," says Philippe Rival, co-founder and CEO of Enlaye.

Rival held several positions at Vinci Construction, eventually rising to a project controls manager working on major infrastructure deliveries such as the Jeffersonville East End Crossing between Indiana and Kentucky and the extension of the Confederation (Rail) Line in Ottawa, Canada.

After earning an MBA at Harvard, Rival founded Enlaye to apply AI and machine learning to construction risk analysis. While contractors can already identify risks, he says AI can analyze a firm's historical project data and current risk portfolio far faster than traditional manual reviews.

Rival said geotechnical, contractual, financial and commercial risks that can require a team of up to five estimators and executives to evaluate can be analyzed early in a project using Enlaye's platform, generating results in about 12 minutes once client data is provided.

The current funding round will let the company further refine the models and systems it uses to predict risk for clients such as Vinci.

"No two contractors have the same history of good past projects and bad past projects, so they all have different burn stories," he says. "The idea of a one-size-fits-all risk matrix doesn't work. People want something that speaks to them, their risk appetite."

Stamatios Liapis, Enlaye's chief technology officer and co-founder, who met Rival in high school in France and later attended Harvard with him, says the platform is designed to augment human judgment rather than replace it.

Risk in construction is shaped by documents, schedules, contracts, relationships and years of experience, he says. To model those interconnected factors, Enlaye uses graph neural networks and multimodal AI.

"The main specialty that they have is that they operate on graph data structures," Liapis says.

He compares the approach to a network of connected nodes and relationships. By analyzing those connections, graph neural networks can identify anomalies and patterns that may indicate future project risks.

Enlaye is entering a growing market for AI-driven construction risk tools. Earlier this year, Trimble expanded its Document Crunch construction document review platform into broader AI-enabled risk management capabilities.

"Risk in construction doesn’t live in any single document or phase. It compounds from bid package to contract to change order to claim, costing the global industry trillions a year in overruns, disputes and rework, said Kleida Martiro, managing director at seed round leader Glasswing Ventures. "Enlaye is the first platform built to manage risk across the entire project lifecycle and across every actor on it."

Rival's former employer, VINCI Group, has supported Enlaye through Leonard, Vinci's innovation and foresight platform, via its seed and Catalyst programs. Their partnership has helped Enlaye deploy its solution with operational teams across the group, which ranks second in the world on ENR's Top 250 International Contractors list.

"Risk management is obviously essential to the success of any project," said Christian Tricoire, director of VINCI Construction Major Projects USA.

"Enlaye’s solution fits naturally within our operations, particularly during the bid phase, where it helps us analyze large volumes of contractual and technical data,” he adds. “It enables us to identify key issues more quickly, better assess our risk exposure, and support more informed decision-making."