Foundational Data and Strategic Vision

ALICE Technologies, Inc. is a pioneer company in the ConTech sector, founded in 2013 as a spin-off of Stanford University. Headquartered in Menlo Park, California, the company introduced the concept of 'Generative Construction', applying advanced computing principles to the management of infrastructure projects. Its founder and CEO, Dr. René Morkos, developed the theoretical foundation of the platform during his PhD at Stanford, focusing on the automation of build logic.

Unlike traditional planning tools, ALICE does not simply digitize Gantt charts, but acts as a parametric optimization engine. The company maintains a private structure and has consolidated global leadership, collaborating with top-level contractors in the energy, transportation and complex building sectors, transforming the way the construction sequence is conceived.

The management team combines civil engineering experience with heuristic search algorithms. Together with Morkos, co-founders and data experts have scaled the platform to handle projects with thousands of simultaneous constraints, allowing the industry to move from static planning to dynamic simulation based on cost and time objectives.

The Simulation and Genetic Algorithms Platform

The core of ALICE is its AI engine that uses genetic algorithms to explore a project's solution space. By uploading a BIM model or task list along with production rules (resources, schedules, and dependencies), the software simulates millions of logistics permutations. This optioneering process identifies the most efficient assembly sequences, detecting bottlenecks that often go unnoticed in conventional Critical Path methods (CPM).

One of the most disruptive capabilities of the platform is the automatic generation of 'What-if' scenarios. In a matter of hours, engineers can evaluate the impact of adding an additional crane, changing the work shift or modifying the concrete pour sequence. This agility allows bidding and execution teams to present optimized plans that reduce total work duration by an average of 10% to 15% and labor costs by a similar margin.