00:07:31 · David Flickinger
[00:07:31] S1: Yeah. It's a it's a great question. And I think at this point, we've all used ChatGPT or Claude or Gemini or some chatbot now. And I think what you find when you use those things is they, first and foremost, typically give a very confident answer. And so one can maybe maybe be persuaded by the confidence of the answer, but these answers, when you use them in the format most people do, they typically lack context. And so what you find yourself doing is adding a bunch of paragraphs. Maybe you're typing it in. Maybe you're even uploading a couple files so that the chatbot has some context about you, your business, your background. Hey. Answer this as if I'm a sophisticated finance professional. Answer this as if I'm a expert in x y z industry. And so you start to get some better context. But I think, ultimately, the difference is having context about your business from a prompt is very different from having context about your business as an infrastructure. In other words, the AI is connected into your CRM, into your ERP, into your databases, into your proposals folders, into your financial documents, and it has all the context about your business. It knows what your employees are doing. So it not only has context about what you're doing, but what your peers, your boss, your subordinates are doing in the business. All that is in this foundational memory layer of your business. And so when you ask a question of the AI then, you get a much different answer, one that's very relevant to your business and one that I think gives you the confidence then to take that answer and go apply it and use your own human judgment where I think a lot of folks who are using these chatbots at best are maybe maybe wasting a little time. Because if they are really the expert, they could often arrive at an answer faster than prompting with an insane amount of individual context every time. But at worst, they're getting an answer that may be different from the right one because it's devoid of context about their particular business and situation, but it's delivered in such a confident way that they might make a bad decision. And that that would be the worst case scenario.
00:10:06 · David Flickinger, Mikk
Ultimately, I think you're seeing a lot of people use AI and in many instances being a little discouraged or confused about how do I do this in an enterprise setting versus just me individually trying to supplement my own skills or work streams with AI. [00:10:24] S0: For me, a true eye opener was the example you gave me when we were doing those pre calls, and I've learned a lot already from you when it comes to AI. But one particular example, and it was the roofing estimator. And you told me the story where... I'll let you tell the story yourself, but this was really, like, where I I've read and studied quite a lot about AI, but this particular example you gave and shared was like, okay. For those who know how to do it, it's it's pretty dangerous in a good way. So please, could you please share the story?
00:10:57 · David Flickinger
[00:10:57] S1: Sure. I think this is a a quintessential example of an agentic AI architecture in a business. And so the example is a roofing business that is constantly bidding on commercial projects. And so they do a lot of new construction, which if you think about it is a bit like a math problem because the building doesn't exist yet except on plans and in specification documents. And so what a good estimator does is they will read those documents. They'll use their homegrown spreadsheets and whatever tools they have. Maybe they're using some third party software tools. But, ultimately, they're putting their own logic in, their own experience, their own materials pricing, their own assumptions about labor, and they're putting forth a proposal based on their best guess or estimate. And it was very clear in this particular business, we could use an AI orchestrator to task AI sub agents to do similar things. And so in this example, we have an orchestrator agent that tasks a plan reader agent that goes and reads these architectural plans, which are quite different actually from a basic PDF with just words. These have numbers. These have measurements. So you need to almost go pixel by pixel to make sure that you've extracted every number correctly because you pick a number wrong out of a spec document, and it could be wildly different outcome in your proposal. So that's one work stream. Second is, okay. Now that we've identified the type of building, can we have an agent go reference all similar projects that this company has done that are like this? Hey. This is an apartment complex building with five large HVAC units on the roof. How many similar buildings have we done like this with this sort of design? Let me go reference those and understand what those are. Another agent is going pulling the latest materials pricing. Another agent is pulling the latest labor pricing. Another agent is trying to understand risks in the job, whether that's geographical risks because it's a 100 miles from the headquarters, and so there's travel, or we need cranes to lift, materials that are unusual because of the dimensions of the building or how close it is to other things. So you have all these agents that are doing tasks. They're routing this up to the orchestrator agent.
00:13:32 · David Flickinger
And, ultimately, that orchestrator agent is applying the rules and logic of the business to generate a proposal. That's really what the estimator is doing, what the human's doing. He's doing all that in his head. And so as we started to iterate this in the business, this example I'm about to give you was sort of the ultimate test of, hey. Does this work? So the estimator, this... Roofing business had really big job. 47 individual buildings in this very large complex in a large metro area in South Florida. And the estimator spent a couple weeks not using AI. Just, hey. This is too big of a project. They're gonna do it on their own, and this AI may not be ready for prime time. And, indeed, we hadn't actually deployed it live where it was being used every day. We were still iterating a bit, but we were pretty sure that we had it right. So we said, well, we'll take all the documents. We'll have it. We're not gonna interact. It'll be a blind test. And so we ran our simulation with the AI, and it took about twenty three minutes to go through this massive pool of documents, 47 plans, specification sheets, and generate a number. It took the human estimator about two weeks to generate a number. And on a $6,500,000 estimate, they were within $400 of each other. And even us working and having designed this system, we were pretty blown away, but it was just proof and validation that this architecture works when the agent has access to the same logic that the business owner or key person in the business has. It can make nearly the same decision, and and why shouldn't it? And so that was the moment, and there's a great story about that that roofing business in the sense that they have leveraged this architecture to grow tremendously over the last several months without having to hire additional people. Now they are replacing people with AI. They're making their estimators more valuable and productive through the supplementation use of these tools and these architectures in their business. But it's a really fun example because the size of the project was so large and the outcome was so close. Twenty three minutes for the AI, two out...
00:15:58 · David Flickinger, Mikk
Two weeks for the human estimator arriving at the same number, and just the cost savings and productivity gain when you think about that applied across a year's worth of work is really, really cool to think about and, is a lot of fun to be on the implementation side and see these success stories. [00:16:18] S0: My question was and still is, we're talking about the senior estimator who has decades of experience doing this particular thing. Can you explain the logic behind the AI? How they're able to learn this all this experience in such a short period of time?
00:16:39 · David Flickinger
[00:16:39] S1: That is really the the key question. And I think in order to answer that question, you have to understand a little bit about the architecture. And so what the AI will do is it will start to build this memory and context layer, which becomes its foundation. So it uses access to all the documents. In this instance, there were over 3,700 proposals that were used to fill this initial memory bank of estimating logic. And so the AI doesn't get tired, doesn't get bored. It goes and extracts all the logic out of 3,700 spreadsheets and distills that out into rules. Similarly, it pulls the language of proposals and extracts that out. It looks at jobs that were lost and jobs that were won, and it looks at not only one jobs, but the job costing after the fact. Hey. We estimated a 16% margin on this job, but it turned out it was 12%. Why? Well, the labor was underestimated. So it can fix that in a subsequent iteration. And so it builds this layer of contextual memories as well as rules, logic gates, things like this that are really the human decision making points that have been distilled out into technological gates or rules. And what you try to do is closely mimic the way that the human makes a decision. And, of course, not every decision that a human makes can be distilled out into an AI rule book. But it turns out that a large majority of these reproducible tasks, whether it's creating proposals or responding to customer inquiries or billing and collections, can be written into rules. And as you start to think about this, and maybe this is something we'll get into later in the in the podcast, is how that knowledge, which we call tribal knowledge or typically lives in the minds of the key people of the business. If you distill that into an architecture that lives and persists and that AI agents can interact with, does that make the business more valuable? And, Mikk, without doing a deep dive into software and coding and databases, what's happening is an AI agent is deploying swarms of sub agents to go look into all these areas of the business that it has knowledge about. And that agent, that orchestrator agent can only give you an answer as good as the systems it has access to. So the answer is if you hadn't connected it to anything, it couldn't give you the same answer as the chief estimator because it would just use its generic logic.