In this presentation, the speaker provides a preview of the private beta of Synthesis Knowledge Agents, focusing on a project experience matcher agent designed to automate the traditionally manual process of matching project experience to pursuit criteria. The demo shows how the agent can accept typed criteria or an uploaded RFP, retrieve relevant projects from internal directories, Deltek, case studies, and even perform radius‑based searches (e.g., finding experience within a 50‑mile radius). Unlike manual workflows that require multiple steps—filtering directories, cross‑referencing typologies, exporting and resorting data—the agent delivers concise, explained results that include reasoning, not just raw lists. The speaker emphasizes the value of the agent’s explanatory output and hints at future enhancements through enrichment of the project information database, which will further boost the agent’s utility.
Chapters
I am very excited to give you a special preview of Synthesis Knowledge Agents in action. If you hadn't joined when Chris explained, the K team has allowed me to start a private beta test of these agents, and wow, did they unlock the next level for us. In this first example, I'm asking my new project experience matcher agent to match project experience to the criteria for a pursuit. In this case, I typed it in. I also could have uploaded the RFP to give it more context. So let's see how it works. Not bad. So this is the kind of task that historically was incredibly manual, right? If you're lucky, you're simply filtering a project directory on Synthesis and extracting the information that you need. If you're like us, you are also pulling information from Deltek, searching folders, and digging through case studies. Asking around for more context and details, cross-referencing typologies and dates, et cetera. And it's not that any one of those steps is particularly difficult, right? It's just that there are a lot of them and they do add up, especially when we're talking about like really specific experience types. I could tell you all of the life science labs that we've completed, but I definitely couldn't tell you every elevator that we've modernized. The other day, we used this agent to find all relevant experience within a 50-mile radius of a specific site, which was a question asked in a recent pursuit. And that sounds simple, but if you've tried to do that manually, What's not, you would probably use AI to make a list of places within that radius and then run a report matching those locations to a field. And then you're exporting data and resorting it for your output. And here it's just answered and it's not only answered, it's explained. And I think that is a really important distinction. You're not just getting a list. You're getting reasoning and information that you can actually work with. In a later case study, I'm also going to talk about project data and enriching our project information database will absolutely supercharge this resource for us.