Christopher Parsons demonstrates "Advanced Project Search" (currently an open beta) that connects people to projects, clients, and consultants by leveraging project-directory data. The system combines agentic retrieval-augmented generation (RAG), query trees, and a disambiguation layer to classify queries (project, person, company, relationship), search structured fields and narrative project descriptions, return answers with citations and explainability (including formulas for aggregated metrics), and support complex multi-criteria and relationship queries (people+projects+companies).
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1804.
Open beta (available to clients via support email).
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Speaking of that, once we kind of work through Advanced Employee Search, the next big obvious bucket of feedback was around Advanced Employee Project Search. So we wanted to connect people to projects, clients and consultants. That was the scope of what we wanted to do here based on the feedback that we were seeing. We also wanted to get better at connecting people to people. So what we didn't do in Advanced Employee Search that we have now added in Advanced Project Search is being able to use people's work experience. So I've worked on six of these NICU or natal intensive care units or whatever the thing is, then I can infer who it got expertise working in NICUs. So advanced project Search, I should say is in beta right now. It's an open beta. So if anyone's on this call, it's a client that would like to use it, just send us an email at support and we can get you added into the beta. And so we're working through beta and that should come out in a general availability once we're, once we're through testing. Okay, so back to the flywheel. What we heard was Project Search. There was a lot of stuff. Give me a list of employees who've worked on academic projects in the last 10 years. What's this Kresge project? Is this project an at risk type? You know, on and on and on, like all these project questions. And we weren't doing a good job of taking advantage of the data and the project in the project directory. So once again we learned that we were going to need a bigger boat. So this got us to the agentic retrieval augmented generation which we talked about before. But we added in a disambiguation layer. So here's the project directory for people who haven't seen it. You know, there's filters on project type and client. If I click onto one of those projects, I get a project profile. So construction costs, square footage descriptions. This is data pulled in from Deltec, open asset, Uninet, AEC 360, et cetera. And in this previous design, this is the design for Advanced Employee Search. We just weren't taking advantage of that data. So we got back to the drawing board. Now we have a project query that came in. What library projects have we designed with atriums? We then categorize that query and instead of is this a general or an employee query, we've just extended that list that we're asking the, the large language model to categorize. So maybe it's a project or is it a company or is it a contact? Is It a relationship. And one of the things that was really important that we added in was this disambiguation layer. So for example, you can imagine a query like Ann Johnson. So is that an employee query? Maybe? Right. Is it a project query? Well, it could be the Ann Johnson Cancer Center. Is it a company? It could be Ann Johnson Structural Engineers. Is it a contact? And it could be Ann Johnson, CEO of Ann Johnson Structural Engineers. Right. And so this disambiguation layer helped us make sure that we understood what the query was actually about and then bring the right knowledge to the AI to help make its make its decisions. So that was a really important piece that we added. But let's just say what library projects have we designed with Atrium's clearcut project query? Now, again, we're doing this query tree thing where we're giving it different tools and we're asking it to help figure out which tool it should use. Project number query, is it a project number query? Is it experience query? Is it a directory filter? It then kind of analyzes that it will loop back. If it feels like it went down the wrong track, it can loop back to the beginning of the query tree and start over. It can also then search the intranet to augment its knowledge and then generate that answer with citations. So as an example, Canal Street Studios, right? This one was pretty good before we did advanced project search. But now things like project number queries, really simple like that's the project number, we're able to find it. Or what's the project number for Braintree in the form of a question. We just tell you it's 1804. Or please list our lead silver or higher projects. Great. So we've got 10 projects. If you look a little more closely in the explainability, we say that, hey, if it matches the filter, lead is silver or gold or platinum. Right. So it's understanding that that silver or higher also means gold or platinum. And then it's able to show exactly where it pulled this data set from. What projects have won AIA awards. And there's one project, and there's a filter for project awards contains AIA or what projects have we built in Massachusetts? Again, we're explaining that matches the filter in the project directory state equals Massachusetts. Okay. Higher ed projects in Massachusetts, over 200,000 square feet. So this one is requiring multiple different queries. Right? And you can just see from the explainability how it's finding those two projects, projects with automated humidification controls. So this one's searching those project descriptions so that those Rich descriptions that are in project. The rich information that's in project descriptions is really, really helpful because as much as capturing structured data in fields about your projects is really, really useful and, and you should be doing that, you can't capture all of it in structured fields. Some of it's just going to be a narrative description. So those project descriptions end up being really, really useful for advanced project search. Okay, now we're looking at some of those relationship queries. So what higher ed projects have we done with lmno? Right? And then I can see, well, we've done two projects that are higher ed and then that's connected to the company Elemento Mechanical Consultants, or what architects have worked on projects in Washington. So first we need to figure out who are the architects. We need to figure out which of those projects were in Washington and then we need to compare that set. So this is kind of searching across two different data sets or what higher ed projects have M. Davison and Ann Johnson worked on together. So we're looking for projects that are higher education, where both of them worked on it. Again, this data is all in that project directory. It's just hard to do these things with traditional software filters and search. Like this ability to use AI to synthesize that information and give you unique glimpses into that data, that's a new thing that's unlocked with AI. Or let's just even take the degree of difficulty up higher. What higher ed projects have M. Davison and Ann Johnson worked on together with bcn? And now we're combining three things, right? The people, the projects, and then the company that they worked with also. So these are really powerful things for marketing, especially if you're doing, you know, some of the SF330 work or you're doing any kind of government work. Like proving that you've worked with these people together in the past is really important. And that's why we went after this so hard in advanced project search? And lastly we put math into it. So what's our average cost per square foot for higher education process projects? And you can see that we kind of not only tell you where we got the data, but we also, from an explainability perspective, show the formula that we use to calculate it.
