Generic AI models, sometimes referred to as a “foundation models”, weren’t built for construction, and the gaps show up in important ways: they aren’t trained on construction data, can't reliably read construction drawings, don’t connect your documents or workflows, have unpredictable costs, and put the burden on you to learn prompt engineering and agent building.
Generic AI isn’t trained on construction data
General-purpose large language models (LLMs), like ChatGPT, Claude, or Gemini, are trained on an impressive amount of data that is publicly available on the internet. However, construction data in drawings, specs, submittals, and RFIs is normally proprietary and largely absent from what LLMs have access to. As a result, they struggle the moment a question or task requires connecting multiple documents, processes, and people the way an experienced project manager or superintendent would.
Reading a drawing isn't reading text
One specific implication of this lack of training data is the known limitations with construction drawings, arguably the most critical documents on a project. A construction drawing communicates through geometry, symbols, line weights, and spatial relationships, not sentences. A swing arc means a door opens one way; the same opening without an arc means something else entirely. Ask multiple general-purpose AI models to count the doors on a single floor plan, on a single sheet, and you’ll get a wide range of responses that all conflict with what an experienced reviewer would count. Reading text on a PDF drawing alone, in the title block or markups, does not solve this critical problem for AI adoption among construction teams.
No preprocessing or agent handoffs for connected workflows
A generic LLM has no persistent understanding of your project. Every question forces it to re-read and re-interpret the raw documents from scratch, because nothing was structured or connected ahead of time into a project-specific knowledge graph. The cost of reprocessing the same documents over and over shows up in your usage bill, growing right along with your document set.
Further, without this connected knowledge graph for each project, even valuable self-coded tools fail to close loops across a real construction workflow. For instance, two separate skills or agents to build a submittal register and review trade partner submittals should have seamless handoff given the connected workflow. With generic AI models, each tool runs without a shared understanding of the project to link them.
Pricing and enablement costs work against you
Generic AI models bill on a credit-based consumption model: you're charged for the text and documents you put in and the output the AI generates. It's nearly impossible to predict how many credits a question or task will take, which makes billing unpredictable, a problem for a business that needs to know its costs for the year and its project budgets well before construction begins. That unpredictability discourages your team from using the tool for fear of hitting a limit.
Building it yourself is a multi-year bet
Some technology teams look at a generic model and assume they can build the construction layer themselves. In practice, to overcome the aforementioned challenges, you would need to hire machine learning engineers, label training data to input into the models, and build a vision model to unlock AI for reading construction drawings. To build and maintain custom agents, team members must learn adequate prompt engineering and agent configuration, and relearn every time the underlying model changes. It's the same reason most GCs don't build their own scheduling or accounting software: some problems are worth buying purpose-built solutions for.
Beyond building, the AI era is still fresh, and most construction workers will require significant training and enablement to navigate this change. Foundational model providers are not qualified to provide this change management support. They can’t train your project teams, and they certainly won’t show up to your jobsites.
How Trunk Tools is different
Trunk Tools closes each of those gaps by design. We've trained proprietary models on real construction data since 2021 so our models learn the relationships between drawings, specs, submittals, and RFIs that a generic model never sees. That includes a vision layer trained on more than 400 object types and 2 million-plus labels specifically to read geometry and symbols in drawings the way a builder does.
Cortex, Trunk Tools' construction intelligence layer, preprocesses each project's full document set once, up front, into a knowledge graph connecting every drawing, spec, submittal, and RFI before anyone asks a question or any agent runs. That shared understanding is what lets our agents hand off to each other automatically instead of running in isolation. For example, TrunkReview detects a new bulletin the moment it lands in the project management system, surfaces every change, including the ones the architect didn't cloud, and hands that finding straight to TrunkRFI with full context preserved, so the agent drafting the RFI isn't starting from scratch either.
Trunk Tools also prices with one predictable, flat annual rate for unlimited usage and users, so cost never depends on how many questions your team asks or how many credits a task happens to burn. And because software alone doesn't drive adoption, our customer success managers onboard your team, train them, and even visit your jobsites to ensure success.
None of this requires your team to learn prompt engineering or agent configuration. Trunk Tools' agents are field-ready from day one of onboarding. As one Suffolk superintendent put it: "I can focus on strategy and higher-level tasks with the time I've gained back."