2 Construction AI Tools That Make More Sense Than LLMs

LLMs are about words, but construction success is about math—here are some number-crunching, deterministic AI tools for contractors.

Large language models (LLMS) now have some toehold in most construction software, either as a glorified enterprise search or help feature, guide-on-the-side analytical tool or sometimes basic agentic artificial intelligence (AI) to automate processes.

But LLMs are problematic. They are not deterministic—they will arrive at different conclusions at different times based on the same data. They hallucinate. They’re slow. And they cannot be easily or reliably audited to reveal how they arrived at a specific decision. They are also resource hogs, and even executives pushing them on the market admit power generation capacity is inadequate to support them at the scale of use projected by AI vendors.

READ MORE ABOUT WHY LLMS ARE A POOR FIT FOR MISSION-CRITICAL WORK

READ MORE ABOUT HOW LLMS CREATE RISK IN ENTERPRISE SOFTWARE

Hyperscalers like Microsoft, Oracle, Google and others are pushing hard on LLMs in part because they have the advantage of being early to market, and are building out infrastructure they hope will protect their revenue as agentic AI bots do the work of many users, impacting per-seat fees based on individual human software users. To what extent this bubble will burst, impacting these major software vendors is the topic for other posts, including this one. But other construction AI tools may more directly and significantly transform the construction industry and do not face constraints like LLMs.

1.       Machine Vision for Construction

Machine vision is AI technology that can analyze, interpret and even take action on visual data from the physical environment or reflective files like PDFs, typically using cameras, lighting, and software. It provides rapid, high-accuracy inspection, guidance, and identification.

Software vendors leverage machine vision broadly across construction applications ranging from progress monitoring, takeoff and safety.

These platforms use computer vision to compare actual site conditions with building information modeling (BIM), ensuring projects stay on schedule and for clash detection and quality assurance in the field.

• OpenSpace and Buildots leverage 360-degree cameras to capture reality on site, which its AI then compares against project schedules and BIM models to identify delays and discrepancies in real-time. Cupix meanwhile takes the output to the next level, augmenting the 360-degree camera images to output 3D images and point clouds.

Other vendors generate point clouds directly, relying not on a camera but light detection

and ranging (LiDAR) paired with supporting software, including:

• Leica BLK360 G2 and the BLKFly for indoor use

• Matterport Pro3 Performance Kit

• FARO Focus Premium:

• NavVis VLX 3

• Trimble Realworks with Trimble Connect and the Trimble X7/X9/X12 terrestrial laser scanners and MX9 mobile mapping systems, integrated with Trimble Perspective or FieldLink

Reality capture paired with machine vision on site can also help manage safety, spotting lack of personal protective equipment (PPE) or other unsafe behaviors to allow corrective action and automating compliance. Examples include construction site camera systems like those from Earthcam that have become a popular choice to automatically monitor safety compliance through machine vision.

Eyrus, meanwhile, ports data from site-based cameras into its site access control application to document workplace activities and manage safety, even extending this with live monitoring services.

In an earlier talk I’d had with Josh Kanner, I learned how his Newmetrix platform, purchased by Oracle, was embedding his AI tools that extend not only imagery but project data using predictive analytics to spot schedule, cost or quality risks before they result in adverse outcomes, deep in the Oracle construction portfolio through Oracle Construction Intelligence Cloud.

Other vendors, including Togal and Patabid use machine vision to identify plan elements for automated takeoff. The solutions pay for themselves in reduced takeoff time and more complete, accurate estimates. Missing a floor, windows or other design elements may result in a low, winning, and very expensive bid.

2.       Construction Schedule Optimization

Using AI to refine a construction schedule does more than ensure on-time milestone and project completion. Intelligently aligning labor, materials, and equipment efficiently minimizes idle time, total budget and can increase quality.

ALICE Technologies uses a brute force computing approach, similar to a chess computing app, to analyze up to 600 million different ways a project may be scheduled to identify the optimal one.  While the solution has been in the market for years, it continues to evolve, with functionality to identify the schedule changes with the highest impact coming online in May of 2026.

Schedule optimization is still important after the project is completed. For trade contractors in particular, optimized field service management schedules are critical for the 70 percent or so that sell aftermarket service contracts. Homeowners overwhelmingly are willing to pay trade contractors for an annual maintenance plan, and in mission critical settings like commercia HVAC and refrigeration, more than 75% of contractors sell subscription-based service plans.

As the number of customers, the number of service visits and restrictive nature of service level agreements (SLAs) give customers guaranteed response times or uptime, how do contractors ensure they are meeting obligations at a manageable cost?

IFS Planning and Schedule Optimization and Oracle Field Service automatically optimize the schedule in real time, intelligently balancing multiple factors including location of each tech, the tools and materials they have on a truck, their skill sets, the nature of each service call and the promises made to the customer.