René Morkos is a second-generation civil engineer who has worked in subsea pipeline construction, automation engineering on a $350 million gas refinery expansion project in Abu Dhabi, and as a project manager in Afghanistan. But over a 23-year career in construction and academia, one question arose: Why can’t we use computer vision and machine learning to help decide the best way to build something?
“ALICE is the world’s first construction ‘optioneering’ platform,” Morkos said on the ENR Critical Path podcast in 2023. “Instead of deciding how many cranes I need or sequence A, B or C, why can’t the computer give you options?”
artificial intelligence
ALICE evolved from several project management scenarios that Morkos used in construction while studying for his PhD at Stanford (he is now an assistant professor of civil and environmental engineering at the Stanford Center for Integrated Facility Engineering). He had developed an algorithm that optimized the use of space on a job site in Amsterdam, and this and other programming automations led to the founding of ALICE Technologies in 2015, which used artificial intelligence algorithms to automate construction sequencing scenarios based on BIM files.
In 2025, the company released ALICE Core, which can take a Primavera P6 XER file and automatically create programming and sequencing scenarios just from that.
“What happened with manufacturing in the 1970s and 1980s — it was digitized, it was automated — now it’s happening in construction. It’s being optimized, digitized and connected,” says Morkos. “The way this happens is slowly: every startup is trying to digitize a piece of the puzzle. But slowly, those pieces are coming together.”
Morkos says that when he founded ALICE, he had to explain to contractors what a startup was, what a pilot project was. At the time, DPR Construction was one of the only contractors with a venture capital arm investing in tech startups. Today, many more are investing in startups, such as Suffolk Construction, which has standardized its complex projects on the ALICE platform.
“High complexity [in a] The project is one case where we use it, and the second is when there is a requirement to accelerate the project,” says Aleksey Chuprov, senior vice president of data and information technology at Suffolk. “Whatever external circumstances are causing a delay in the project, it needs to be recovered, and ALICE is great in those scenarios where a project baseline has been made and a goal has been set. You must bring it back to this target, and [ALICE] it helps you find the best ways to get it back.”
While Morkos is a dedicated construction technologist who has been working in AI, machine learning and computer vision technologies for 30 years and is generally an advocate for AI in construction, he is not among those who believe AI will dramatically change everyone’s workflow in the short term. For Morkos, there is still a need for human knowledge in tasks like programming.
“There is absolute progress,” he says. “Machines get better, but then we realize that these are not the only things we have to consider, and these specific problems are not solved. So we go to work on the next layer. The truth is that this has been the pattern for 50 years.”
But Morkos thinks the coordination and sequencing approaches that began with Gantt charts in the 1890s and evolved into critical path scheduling in the 1940s still have room for improvement.
“Think about running [a schedule] through a computer simulation, and then look at the [critical path method]which is like 16 lines of code. It is very clear that the level of complexity does not match what is happening in reality”, he says.
