How AI Helps Manage Large AEC Detail Libraries

Most firms that have invested in a detail library for a decade or more eventually end up with the same problem: the library is too big to navigate. A senior BIM manager I spoke with recently told me their container model has over 1,000 drafting views. Engineers don't search through 1,000 drafting views. They open three or four they remember, scroll until something looks close, and if nothing matches in the first few minutes they redraw the detail. The library is enormous. The portion of the library that actually gets used is tiny.

This is the paradox of large detail libraries. The bigger they get, the less useful they are per query. A 50-detail library is browsable. A 1,000-detail library is searchable only by knowing exactly what you're looking for, which engineers sometimes don't, because they're early in design and still figuring out what they need.

This is the problem AI is uniquely well-suited to solve. Not because AI is inherently smarter than an engineer, but because AI doesn't get tired of reading 1,000 drafting view names. It can read every single one of them, every time you start work on a project, and tell you which ones are statistically most likely to be relevant to the project you have open right now.

The two moments in a project where this matters most are at the DD and CD phases, the start and the polish.

At Design Development, an engineer is laying out the first version of a system. They know they're designing a chilled water plant, they know roughly how many AHUs the building has, and they know the architectural shell. What they don't yet know is which details from the firm's library should be on the sheets. Most engineers handle this by opening a recent similar project and copying its sheet set as a template. That works, but it carries forward all the same gaps and oversights from that earlier project. AI lets you do something better, start with a recommendation pulled from the entire firm's history rather than one engineer's memory. Recs reads the equipment families in your active model, air handlers, pumps, VAVs, panelboards, fire pumps, and matches them against every detail your firm has used on similar equipment in past projects. By the time you've placed a few major pieces of equipment in the Revit model, Recs is already suggesting the 20 details most commonly used alongside that equipment in your firm's past work. It's a jumping-off point that reflects how your firm actually designs, not how one engineer remembered.

At Construction Documents, the role flips. The sheets are mostly built, the systems are mostly designed, and the engineer is doing a final pass to make sure nothing is missing. This is where firms historically catch problems by sending the package to a senior engineer for QC review and hoping they spot the missing details. AI gives you a sanity check that runs continuously. Recs scans the equipment in your model and the details on your sheets, compares them against patterns from similar past projects, and flags details that engineers in your firm typically include but that aren't on your set yet. "Other projects with this AHU configuration usually included a condensate trap detail. You don't have one yet." It's a second pair of eyes that has read every sheet your firm has ever published.

The reason this works is that Recs doesn't recommend in a vacuum. It uses the actual contents of your active Revit model: equipment families, family names, system names, discipline metadata, and annotations as the input to the recommendation. It's not guessing what kind of project you're on. It can see the Liebert CRAH units, the Cummins generators, the schedule of pumps. From that input, it cross-references the firm's container model and synced past projects to find details that have appeared on sheets where similar equipment was present. The output is firm-specific. A mechanical engineer at Firm A working on a data center sees recommendations drawn from Firm A's data center work. They don't see generic data center details from the broader internet, and they don't see another firm's standards. The recommendations are scoped to your firm's library because your firm's library is the source of truth.

The deeper benefit shows up over time. The more your firm uses Recs, the more the system learns which details actually get accepted into final sheet sets versus which ones are recommended but never used. Over a year, the recommendations get sharper because the corpus the AI is learning from is growing — every new project adds another data point. A firm that has been running Details for two years will have a recommendation engine that knows their work better than any new hire could in five.

Large detail libraries don't need to be smaller. They need to be smarter. AI is the layer that finally turns the thousands of details your firm has accumulated over decades into an asset rather than a liability.