For several decades, this construction firm has built its reputation on the principle of continuous improvement, a mindset that once led the company to build its own widely-adopted quality control program, and eventually pointed them toward AI.
Setting up a project's Quality Assurance Plan had stayed a manual, labor-intensive task: a superintendent working through thousands of pages of specifications and drawings, then deciding which items from a QA/QC library of over 200 inspections, preinstallation and coordination meetings, trade mock-ups actually applied to that project. The team came to RapidCanvas to fix that, not by adding headcount, but by giving their own internal technology team the tools to build the solution themselves.
Together the firm and RapidCanvas are building an AI-assisted system that reads project specifications and drawings to draft the right QA/QC activities in a first pass, keeps a human reviewer holding the pen, and pushes the approved plan straight into Procore.
Challenges Faced
Growth had outpaced the process. A quality function sized for an earlier, smaller version of the company was now stretched across many more projects and the manual systems built to hold it together were starting to show cracks.
Days of manual reading, every project: Setting up a single Quality Assurance Plan meant working through a document set running to a specifications book and a drawing set over 600 pages long, and then matching it against a QA/QC library of over 200 possible items, all by hand.
Coverage varied by reader: What ended up on the plan depended on one superintendent's memory of the QA library and the trade. It was hard to standardize, and easy for teams to go their own way.
A miss was invisible: Nothing flagged a required inspection that had been left off the plan, until it mattered on site.
No traceability: There was no record of why an item was on the plan, or who had decided it.
No clean input to build on: Construction specifications are wildly variable and often incomplete. Temporary power/lighting, for instance, has no spec of its own. Any automation would need to tolerate messy, inconsistent input as a starting assumption, not an edge case to handle later.
Solution Implemented
The solution works on two levels: a simple experience for the reviewer, and a more complex engine underneath doing the heavy lifting.
For the team, day-to-day: The tool reads a project's specifications and drawings and proposes the required QA/QC activities automatically, checked against the full QA/QC library. A reviewer curates the plan in a simple, human-in-the-loop interface by accepting or rejecting items with grouped reasons, seeing excluded items and why they were filtered, and adding any back with one click. This then pushes the approved plan straight into Procore as action items.
Under the hood: The engine uses retrieval-based AI over project specifications and drawings to derive quality plan components by trade, drawing on two evidence channels: specifications establish whether a requirement exists and drawings catch scope that shows up only on structural sheets. It's built recall-first: missing a required inspection costs more than proposing one that turns out unnecessary, so precision gets recovered later through review rather than enforced strictly up front.
Every proposed item carries a citation and a confidence rating back to its source document, and extraction/determination rules are version-controlled and stamped into every run's audit record.
The first release is deliberately focused, creating action items in Procore, where the existing template already carries the surrounding structure, leaving automatic scheduling and full field population as later enhancements.
Results and Benefits
The metrics matter, but the more telling shift is what they've done to the actual workday. A superintendent's job used to include being the sole gatekeeper of a 200-plus-item template QA checklist with no way to know a gap existed until it showed up on site. That weight is gone. The plan now starts as a draft grounded in the spec and drawing set for that project, with every item traceable back to why it's there, so review time goes toward judgment calls a person is best suited to make, not toward re-deriving the list from scratch.
That shows up most clearly in what didn't happen: no required inspection was missed across the run, and the tool's few over-inclusions came flagged with their own uncertainty rather than dumped on the reviewer to sort out blind. For a quality team that had been stretched thin relative to the size of the business, that's the difference between reactive firefighting and actually keeping pace.
Scored against a superintendent-reviewed answer key based on the primary success criteria the team agreed on before the build, the solution met all its targets.
Citation validity: 100% of citations resolve to a real spec section or drawing sheet, zero fabricated by AI, 98.7% verbatim.
Turnaround time: This stands at ~56 minutes from upload to draft-ready. This is 4.3× under the 4-hour target.
Human sign-off: 100% of items reaching Procore are human-reviewed, with a server-side approved-only filter and a per-reviewer audit log.
The full solution was created in 10 weeks including design, MVP, validation, and go-live and now runs on roughly 10 hours a month of support, a small ongoing footprint for a tool that's taken over the heaviest, most error-prone part of a recurring task.
Conclusion
This engagement is a good example of what “starting focused” can look like in practice. Rather than trying to solve quality management end to end, the team scoped a first release tightly: identify the right quality activities and land clean, reviewer-approved action items in Procore, and hit every success criterion they'd agreed on going in.
That same pattern is already pointed at what's next: pulling schedule data into the loop so it can be cross-referenced against the risk matrix to flag safety teams when high-risk activity dates shift, closing the loop so executed activity automatically keeps the master quality matrix current, and a single natural-language search layer spanning Procore, document stores, CRM, and specifications for cross-project lessons learned. Starting from a focused first release, the engagement lays a structured, scalable data foundation for quality across the business, with the customer's own team building the capability in-house on the RapidCanvas platform.
