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ENR Feature: ALICE Technologies Raises $30M in Series B Funding

Construction technology leader ALICE Technologies today announced a $30M funding round led by Vanedge Capital. 

FEATURED ON ENR JUNE 16, 2022

By Jeff Rubenstone

AI-based scheduling and project management tech firm ALICE Technologies announced June 13 that is has raised $30 million in a Series B funding round.

ALICE specializes in an AI-driven analysis of preconstruction data to generate a full construction sequencing plan. Everything from erection of the structure to the management of labor and materials is handled by the AI, which can adjust parameters to find more optimal scheduling paths.

The funding round was led by Vanedge Capital, with new investors including Bouygues, Gaingels, GRID Capital, JLL Spark and MetaPlanet. They were joined by existing investors Future Ventures, Merus Capital and Rising Tide. ALICE has grown significantly in recent years, reporting annual recurring revenue growth of 270% in 2021.

“The ALICE platform gives general contractors the ability to easily explore construction choices and chart a path that best balances risk, time and cost,” said Paul Lee, managing partner at Vanedge Capital in a press statement. “This is a distinctive, high-growth business led by a uniquely qualified team, and we’re excited to support ALICE’s growth and success.”

The company has more to its momentum than a successful funding round. According to founder and CEO Rene Morkos, ALICE Technologies has seen significant growth in its customer base, which he attributes to its recently introduced project management capabilities. “We’ve really hit our stride,” he says. “The release of [new features] to manage a project during construction has really been a critical turning point for us.”

Morkos says that in the past users of ALICE had been impressed by the AI-driven scheduling, but had balked when they found out the company was only tackling preconstruction planning. The new project management features now allow for the AI to reassess the schedule throughout the project during construction. “Clients said to us 'we’d like to go build per what ALICE is telling us,' but we had to reply we didn’t have that part yet,” he says. “Now we have the whole offering.”

Given the scale of variables it is able to ingest, Morkos says ALICE is aimed primarily at projects greater than $100 million in value, mostly commercial construction and infrastructure. It has been used for some parts of the HS2 high-speed rail project in the UK, and has been deployed on other major infrastructure projects around the world, including water treatment plants and solar and wind energy installations for major contractors such as Bouygues and Skanska.

Morkos says the company has also been making inroads into the industrial sector, where ALICE is being used for work on oil and gas projects. And the complexity of these jobs has been an interesting problem to tackle with his team. “The challenge with those projects is the sheer data,” he says. While many attempts to build simulations of construction work have been overwhelmed with variables and complex rulesets, Morkos says ALICE can work from a smaller set of heuristics to process the information. “You can have five set rules that allow you to generate 5,000 tasks, but even with that force multiplier, on [an industrial job] you’re still taking 50,000 inputs.”

The ALICE team is working to build out its APIs to ingest the large volume of data such industrial projects generate, and Morkos says they are making progress and expect to see the AI tackle larger oil and gas projects going forward.

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