In this presentation, the speaker explains how their architecture/engineering firm tackled the growing pressure of longer, more technical proposals and tighter procurement timelines without expanding staff. They adopted the AI Search Beta and built a curated intranet community called Archie (Synthesis) to house a proposal language library. By structuring content into typology‑specific guides, automating updates through their task management system, and leveraging AI Search filters and knowledge agents, they turned raw documents into instantly answerable, authentic knowledge. The speaker highlights a real‑world example where AI Search surfaced a relevant staff‑meeting transcript to answer an acoustical attenuation question for an affordable housing project, demonstrating the AI flywheel of capture‑surface‑reuse. A quantitative test showed a reduction in RFI response time from 15 hours to 3 hours, with answer quality equal or superior to the manual process. The system also reduced variability among team members, boosted creativity, and streamlined onboarding of new staff by providing consistent, searchable knowledge. The overall narrative underscores that curated, structured data outperforms bulk data, and that integrating AI Search into daily workflow creates a self‑reinforcing cycle of knowledge improvement and efficiency.
Chapters
Around 2023 into 2024, we started feeling real pressure in brand communications. Proposals were getting longer, more detailed, more technical. Clients were asking really great questions. Procurement processes were more structured. And they weren't just looking for expertise and relationships. They were really looking for demonstrated thinking. And at the same time, proposals are how we sustain growth, and we were growing really rapidly. So the expectation was do more, do it faster, and make it better. But our team size wasn't increasing at the same rate as the firm. So something had to change. We had to find a way to improve both speed and quality without simply adding more people to the team. And then AI Search Beta was released and a light bulb went off for us. We could build this thing within our existing knowledge base and freely input all of the conf— confidential information that felt just absolutely crazy to release to a startup tool. So the solution that we ultimately arrived at was to create a community on Archie, and that's our name for our Synthesis intranet, and devote it to proposal language library. Initially, we created one giant guide within that community, and then we very quickly realized that we should create different guides for different work types. And the reason that we did that was because we wanted to use search filters for AI search and eventually use knowledge agents to recall that typology-specific content. And then we started filling in the guides with curated, structured information. And within our separate task management platform, we made updating this language an automated task that appears as part of our proposal closeout process. So we made it part of our workflow, and therefore, this database of information increases value incrementally, and we don't have to do this once in a blue moon, massive effort to go in there and add information. So now with AI Search, instead of digging digging through documents, you can ask a question, get back a response that is already assembled from the structured content. And as a marketer, authenticity is incredibly important to us. And in this era of just endless streams of AI-generated content, this answer is authentically ours. It is grounded in our knowledge. And our knowledge alone. It is grounded in our very real project experience. It reflects how we currently talk about our work, and it's something that you can use as a starting point, or in some cases, even as the end product. So instead of spending time searching, copying, editing, you're spending time refining. You're working from something that already has the context to it, and that changes both the speed and the quality of the output. Uh, it also reduces variability among team members. Different people are no longer starting from completely different places. As I started sharing this with people, one of the most common questions that I got was, why not just put all of your past proposals into a library and use that knowledge? And one of the clearest lessons here was that more data does not automatically produce better results. And in many cases, it does the opposite of that. Because our knowledge evolves. What we said about a topic 2 or 3 years ago may not reflect how we think about it today. We find out new information. Best practices change. Processes improve. Language gets refined. And so if we simply include everything, we are introducing a certain level of noise and inconsistency. And it doesn't make the product better. What mattered more has been creating content that has better structure and is more curated. And once we started using AI Search really regularly, and I do mean this is part of our daily workflow, to help us draft content. Something kind of fun and unexpected happened. We were doing a proposal for an affordable housing project that was along a major freeway. And the proposal asked if we had experience with acoustical attenuation for this particular circumstance. And normally the process would have been to email our very busy housing team right away. But on a whim, we decided, hey, let's put this question into synthesis and see if by any chance there's any result. And immediately AI Search surfaced information from a firm-wide staff meeting video transcript where the housing team had presented about an affordable project they were working on that happened to be along a busy freeway. And they included in that presentation how they had dealt with the noise. So instead of sending the team a vague question, we sent them a draft response for them to review. And that's a totally different starting point. This kind of knowledge in particular can be really easy to overlook, but when we have a tool that can automate how it is captured and how it's surfaced, it becomes something valuable. And instead of just disappearing when the meeting ends, it becomes part of our firm's collective knowledge. So in this way, we see the AI flywheel at work, right? Knowledge gets captured. In this case, it was the video transcript from our all-staff meeting. Then AI helps surface and connect it to real work in real time. We formalize it into something reusable. In this instance, it was an update to our case study about that particular project, which also lives on our intranet. And so that improves the outputs for the next cycle. So the system is getting better over time because the quality of the information is improving. We saw meaningful results very quickly. And the same month that we started building that initial library, we received an RFI from a potential client, and it was an Excel document with multiple tabs and well over 100 questions. We were still building the initial library, so we answered this the old way. And all in all, it took the team about 15 hours to complete. We decided that this was a great test for our new library. So after it was completed, we created a column for the AI search results, and we re-completed the RFI using AI search. And it took about 3 hours. And what was really surprising, we went through and graded each answer. And for the most part, the answers were as good, if not better, than what we had already inputted. And in the cases where they weren't, it was usually just because we hadn't created the information within our library yet. So we are now spending a lot less time on recall and compiling and more time on improving and refining. So we're producing better responses. And more consistent language. Not only that, but as we saw from that housing example, the creativity of the response improved as we had more sources of information. And now, as we add new team members, which we did recently, they can add this— access this information instead of relying solely on individual guidance from other team members.
