Trunk Tools brings artificial intelligence directly into construction workflows, helping project teams find information, review documents, track changes, and work more efficiently with the project data they already use.
Construction projects generate a constant flow of information across drawings, specifications, RFIs, submittals, schedules, contracts, and revisions. Finding the right information often means searching through multiple documents and software systems, while new information continues to arrive throughout the project.
Trunk Tools is designed specifically for this environment. Its AI works with construction project data and provides tools for common tasks such as answering questions about project documents, comparing drawing revisions, and reviewing submittals. Rather than functioning as a general-purpose chatbot, the platform is built around the information and workflows construction teams already use.
Trunk Tools does not require a construction company to replace its existing project management and document systems. The platform can connect with services such as Procore, Autodesk Construction Cloud, Microsoft SharePoint, Box, Dropbox, and Egnyte, allowing project information stored in those systems to become available to its AI tools.
This approach allows Trunk Tools to act as an intelligence layer over an existing construction technology stack. Project teams can continue maintaining drawings, specifications, RFIs, and other records in their established systems while using Trunk Tools to search, analyze, and work with that information in new ways.
TrunkText gives construction teams a conversational way to search through project information. Instead of manually searching drawings, specifications, RFIs, submittals, schedules, and contracts, team members can ask questions in ordinary language and receive answers based on the project's own documentation.
This can be especially useful in the field, where someone may need an answer without spending time searching through a large drawing set or specification book. TrunkText also connects its responses back to the relevant project sources, allowing the information to be checked against the original documents.
Construction drawings change throughout a project, and identifying every change between revisions can be time-consuming. TrunkReview uses AI to compare drawing versions and identify differences between the previous and newly issued sets.
The system provides visual comparisons along with written descriptions of the changes, including changes that may not have been specifically clouded on the revised drawings. This can help project teams identify potential impacts on pricing, procurement, scheduling, and work already underway before those changes create larger problems in the field.
Submittal review requires teams to compare proposed products, materials, and equipment against project specifications and other requirements. TrunkSubmittal uses AI to perform an initial review and bring relevant information together for the person responsible for making the decision.
It can identify applicable specification requirements and RFI responses, compare them with the submitted information, and flag potential compliance issues or missing information. The construction professional remains responsible for the review, but the AI can reduce the manual work required to find and compare the relevant project information.
Cortex is the underlying AI platform that powers Trunk Tools. Rather than treating drawings, specifications, RFIs, submittals, schedules, and other project records as isolated files, Cortex is designed to understand the relationships between them and build a connected representation of the project.
This gives the individual Trunk Tools products a shared foundation. A question asked through TrunkText, for example, can draw from information across different types of project documents because Cortex connects that information within the larger project context.
As project information is added or synchronized from systems such as Procore and Autodesk Construction Cloud, Cortex continually organizes that information into what Trunk Tools describes as a construction knowledge graph. Drawings can be connected with specifications, RFIs, submittals, schedule activities, and other related records instead of remaining separate pieces of information.
This is what makes the broader platform particularly interesting. Cortex is intended to become a central intelligence layer for the construction project, while specialized AI tools use that shared project knowledge to perform different tasks throughout pre-construction and construction.
Trunk Tools demonstrates how AI can become part of the construction workflow without requiring teams to replace the systems they already rely on. By connecting project information through Cortex and applying specialized tools to tasks such as project search, drawing review, and submittal review, the platform can help teams spend less time finding and comparing information while keeping experienced professionals in control of project decisions.
For developers and contractors exploring AI, platforms like Trunk Tools also show that adopting new technology does not necessarily mean rebuilding an entire technology stack. The opportunity may be in connecting existing project data with new tools that make that information easier to understand and use.
Dream Cloud works with developers, contractors, and other organizations to explore and implement emerging technologies across the development and construction process. We help teams understand what new platforms can do, evaluate how they fit with existing workflows and software, and identify opportunities for integration and implementation.
If your organization is interested in Trunk Tools or exploring AI and other emerging construction technologies, contact Dream Cloud to discuss your current workflow and where new technology may be useful.
You can also learn more about the platform directly from the official Trunk Tools website.
Reflex Robotics is deploying general-purpose humanoid robots into logistics and industrial environments, using real-world work and human supervision to help its systems become increasingly capable.
Robots are getting more capable, but the next challenge is giving them enough experience in the environments where they will actually work.
Creating real-world deployment opportunities where robots can perform practical tasks and generate valuable training data for physical AI.
Developing highly automated food production facilities that combine robotics, industry expertise, and regional partnerships to reduce costs and supply prepared food at commercial scale.
