Name: Evaluating GovCon AI: A Playbook from the Inside
Uploaded: 2025-08-19T16:18:18.202Z
Duration: 38 min 28 s
Description: Evaluating GovCon AI: A Playbook from the Inside

Transcript for "Evaluating GovCon AI: A Playbook from the Inside": Awesome. Well, it is 11:03. So I'm sure, we'll have a few more folks trickling in here, but, we'd love to go ahead and get started. Again, wanted to thank, everyone who has hopped in. Really appreciate you being here, and I think we're in for a fantastic session. And the topic of today's session is evaluating GovCon AI, a playbook from the inside. And today, I am here, with with Greg James, who is a senior business operations manager at Albers Aerospace. Fantastic company. Greg is leading business operations and the business intelligence units, driving proposal development, compliance, and operational excellence across the entirety of the federal conduct lifecycle. Greg has had some, pretty impressive accomplishments during his time at Albers. He has, got their proposal win rates, improved by 28%, grown their pipeline by a 150, has, over ten years of experience managing cross functional teams. And importantly to the discussion today has been a huge champion in, evaluating and adopting AI at the company. So Greg, thanks again for joining us. Really glad that you're doing this. Yeah. Absolutely. Thanks for having me. Awesome. So, we're gonna go ahead and jump right into the session today. And and just to set the stage, really what we're looking to, accomplish is to learn from Greg, and you hear a little bit about, how he and Albert generally evaluated, AI, incorporate it into their business, thought about building versus buying, and much more. And so, Greg, just to kick things off here, before AI was even on the table, like, what, was the catalyst for considering this? Like, what what what were the operational challenges that you were facing? Yeah. So as a as a small business in GovCon or small business anywhere, you're you're burdened with the fact that business has its peaks and valleys. And so in those peaks, you're you're ramping up. And as you guys have heard me say, we're running fast with scissors and and hoping that we don't fall. And so in that moment, we're we're trying to get as much done as we we possibly can. We have a a great team, a good part charging team that is willing to take on more than than they're actually required to to get the work done. So it's you wanna be able to manage your resources and your workload competitively so that in those valleys, you don't have more resources than that that are needed. So as as we go through those peaks and valleys and proposals are coming through like crazy, most of the GovCon space know that the government loves to push out proposals to be due after Thanksgiving or after Christmas, and so it it ramps up. And so to to kind of mitigate and manage the the PTO of your team to not burn everyone out, there was kind of that that bottleneck of how do we how do we do this, how do we implement a kind of a force multiplier to to help us do this and and push forward without burning everyone out and keeping that same loyalty and dedication that we have with the entire team. So that was the the primary function of it. The other function was as we grow internally, we we don't know what we don't know at this point. So internally, how can we use another resource whether it's another body or a tool like AI to kinda help us go through this? And then on top of that, how do we do this and stay compliant with the government with the type of information that that we hold, within our databases? So those were the the the main core, areas that that I was tasked with. Understood. And and so maybe just to sum that up, the the first piece of that is you already have the expertise on the team. Team's doing fantastic work, but just due to the the nature of the beast, right, of of working in the federal contracting space, you've got, these super tight deadlines, things dropping, seemingly out of nowhere, and you need to figure out a way to scale that. Right? So that you didn't face major burnout in the team. And the the second piece being that you're just a a curious organization. Right? And they're they're trying to figure out how you can improve things perpetually, and this is one of those areas where it seems like it help it could help with both. Absolutely. Absolutely. It's one of those to where as we're growing, we we wanna stay in the forefront. And so, as a small business, you're constantly trying to increase your capabilities, increase your past performance, and then not just your capability on the execution side, but internally operationally, how do you how do you increase those capabilities to to keep up with execution. So keeping those in balance was was very important for us because our culture inside of Albers is is very team and family oriented. So we want to focus to prevent burnout and and kinda take care of our people while also kinda charging hard and and being the tip of the spear going forward and hope for these new opportunities, capabilities, and so forth. Understood. And, something I hear hear a lot just, from from the companies who are fortunate enough to work with at GovDash is, and others that are, like, evaluating us, is, that the individual contributors on some of these teams, like capture folks or, proposal managers or writers, they seem to hit that kind of, that burnout point much earlier. And then there's a a challenge in some organizations to kinda make it clear to the broader organization, one, that there is, like, a a problem there, and then also that AI might be a, might be a potential solution. And so I'm curious if there were specific kind of triggers in the business that, allowed you to then go to the the router team, to talk through, hey, would it make sense for us to to start looking at these things? Or is it that the organization just kind of organically, evolved into to thinking about things that way? No. It it definitely wasn't organic. It was kind of a a forcing function, due to due to bandwidth, due to demand power and so forth. But also, in the larger companies, they have your your true pursuit managers, your bid pursuit managers, and then you have capture that goes into business development, so forth that whole life cycle. So in a smaller company, we're wearing multiple hats. So as a small company, we're bringing in resources that have already gained their experience elsewhere. And so trying to bridge that gap to show, hey, this is how it was done at a larger company with multiple teams, multiple resources for each opportunity that you go after. We're a lot smaller than that, so you have to wear multiple hats. And so this was a function to where we we could kinda leverage AI to help us train internally and kinda bring those people up to speed. So the the bidding proposal pursuit leads can can kinda learn on the capture side, and our true capture managers who are over burned with multiple opportunities that they're pursuing, they can use the the business intelligence side of AI to do the deep dive into competitors, competition, incumbents, so forth, and and pull those key questions out as far as as a small business, are we positioned to prime this or should we sub? And if we sub, who are those teams? Who do we go go after? Who has the best potential to win on this? What's their their on it? How we go after those teams. And so it get gave us the ability to make more strategic decisions internally, instead of what we used to call it, just throwing spaghetti at the wall and see what sticks. We wanna kinda focus that and be more targeted, and this has dramatically helped us do that. Identifying who those those the competition is and the customer to find out their core needs, not just whether they push out there, but whether they actually need based on historical information, historical wins, historical losses, and and issues with previous contracts. Got it. And so once you reach that point as an organization where you you thought about, okay. Hey. Like, this is something we do wanna go for. How did you approach deciding whether you were gonna build this in house or partner with a vendor? Yeah. So that's a that's a huge question. Going into this, I I had a very shallow depth of AI experience. It was just OpenAI came out, chat t p t came out, people are playing with it, you get the new stories and all that but it's it's it's the learning process that you kinda organically have to learn on your own because there's not a it's changing every single day. Every day a new technology comes out, new platform comes out, a new LM comes out. And so initially looking at it in our c suite, they were extremely scared of AI just because of the spaces we work in is very controlled with compliance, cybersecurity, and classified information, CUI, stuff like that. So they were very scared in the sense that we don't know what the cloud is. We we don't know where the information is stored. We don't know just just trying to have access to it. Like, who has access to this stuff? We don't trust it, so we're not gonna use it. But just watching AI progress as rapidly as it did, which was extremely impressive, But watching it progress the way that it did and mature the way that it did, it became a hey. This is more of a a a need to have versus I like to have. And so, if we're gonna do this to stay on the forefront of our industry, we have to kinda incorporate this, but let's let's do it smart. Let's put some guardrails up and let's figure out who do we pick, who's out there, who's the the tip of the spear so to speak, and how do you identify that when these are changing every single day. And so we did, I would say, eight to 10 demos for multiple companies. GoDash being one of them and GoDash was actually right in the middle of the stack. So it wasn't just the last one we we chose. We liked it. We went with it. It goes in the middle. So there is pretty intensive evaluation process that we we went through and the thing that stuck out or stood out to me between all the others against GovDash is it felt like these were AI engineers, programmers, whatever the label is nowadays. These are these are AI people who tried to create a proposal platform versus GovDash who came out as, hey, this is a proposal platform that true proposal industry professionals got together with AI engineers and made this work. The life cycle fits exactly how we use it. The the bid match feature, it it knows how to scrub sam.gov, gov and so forth and and pick those next codes that actually apply to us. It's not just keywords, it's context based. So it understood what ours aerospace did as a whole and picked out bid matches that match that, not just keywords because we had that keyword listed on document somewhere. It actually put some thought around it and that was huge for us on the capture side, like I mentioned a minute ago, on bandwidth. It helps us identify those opportunities and create a true gate for qualification to make sure, hey. We want to go after this because we want to, not just because it's out there. And so that's where we kinda put the run spaghetti at the wall. We're able to kinda focus it and and target it. And so Gil Dash on top of that was key as far as understanding the process, but also with the rapid growth that you guys have had and being able to stay personal with the customer, we we've got a couple, I think, extra guardrails that we asked you guys to kinda code in, put in for us that were were different just because we were, I guess, hypersensitive around CUI and control information like that. But we wanna make sure that if GovDash has this AI bot that lives inside of Microsoft Word, is it always reading the documents? So I kinda see everything that pops up on our screen. And we didn't know the answer, and IT were obviously very scared of AI. And so Yep. We came to you guys and asked for a a custom solution that we had to have a manual override to make sure the document on the screen did not have classified control information before the bot was even able to be activated. So those are some of the things that as soon as we asked, I think it was a day and a half later, that solution got rolled out to us and just pushed through an update. And I'm sure that's our IT team, and that immediately had their buy in as far as, hey. Any concern we have, they're willing and capable to address it, and this is this is great. So from that standpoint, IT at that point, they didn't know what they didn't know also. And so they weren't able to give us the list of, hey. These are our risk and concerns. We're gonna see these addressed first. But they knew that if those risks and concerns came up, Govdash is more than willing to step up and and and lead the charge and have this fixed for us. So that's the only way you guys to choose GovDash. On that topic, I I think something that teams, really teams that are, procuring system like this right now struggle with is winning over the IT team. As you said, it's, IT teams are, notorious for, rightly so, being, like, very protective over the business, very protective of where data is, being used and where it's flowing to. And, I know you just shared a little bit about, like, what ultimately got them comfortable. Do you have any suggestions just to the the broader group, and how to engage with the IT team when when when going and evaluating products like this, or even when and potentially looking to throw something your own. I think, it would just be helpful for the group to understand how you went about approaching them, how you went about providing this information to them, if you have any thoughts there. Yeah. I think initially the the biggest buy in was on the return on investment, pitch that we made. And so it's showing the actual opportunities that that fit perfectly for our our future pipeline that we want to build these into and show them the life cycle of it and how it kinda go from cradle to grave in in one platform versus jumping around using spreadsheets or anything like that. So getting getting that buy in first from the overall BD and operations team, and then they'll get that as far as, right, now we need to position this to IT. What what are their concerns? What are their risks? How do we address that? And working with you guys, y'all actually came up with kind of a a fact of frequently asked question list for for IT. And so I was able to provide those documents to them that showed where you currently are in your your certifications, your structure, your onboarding process for FedRAMP and so forth. And so they saw that and they immediately understood, okay, this is where GovDash mind is. This is what they're thinking as far as cybersecurity, what's required, and all that. They're on the right path, but we still have some questions. And so that was way above my head. So I just I coordinated the the talk with your your team and and IT, with our team. They sat down together. I was in there for the first ten minutes, and it was a completely different language I didn't understand, so I stepped out. But they initially just a very organic, hey. What are your concerns? How can we help? And so every concern that our IT director brought up, GovDash immediately had an answer for or had a response of, we don't know just yet, but we'll get to you ASAP. And as soon as the call was over, within thirty minutes, all those questions were answered. And so that's where some of the requirements that we had, honestly, it was GovDash met the requirements as far as cyber cybersecurity and this and so forth, but we had some extras. So we wanna make it a little bit stronger, and that's where that that c y function came in. And so it wasn't a function that GovDash didn't do it. It was just an an extra function that we just wanted to make ourselves feel secure because AI is it's scary. And so Yeah. Just to to help everybody get the warm and fuzzy and say, hey. We do have AI, but we've also got these additional steps. It's compliant, but we have additional steps to make it stronger. So if something happens, a human element and not somebody just opened the wrong document, now AI is visible and it's reading the documents, stuff like that. So that was a big thing. It's just showing them that there was a return on investment. So there's a buy in from the overall corporate BD operations team. So now IT said or sees it and they understand, okay. They really wanna go after this. There is a return. I can't just say a hard no because I'm scared of AI. I actually need to look at this and figure out how can we make this work. And so that was the biggest thing was getting that that pitch first to our our beating operations teams and then go to AI or I'm sorry. They go to to IT after that. Got it. And so once we've got approvals from, IT and organization spot in, we're we're ready to go, we're ready to start. Something that we think about at GovDash a lot is, okay, how do we then help teams roll this thing out and, get get people in on the proposal team and even beyond that, really engaged in understanding of how this can be useful for their workflows. Change management is always hard. I think there's a case that it's especially hard in this, this new AI world that we're in. So I'd love to know, like, how you went about rolling out AI to the broader organization, without creating resistance? Yeah. So initially, I I don't think resistance at the team level was an issue because they were very eager to use it because they they knew it, make their life easier. So I I think the resistance was from the overall c suite and our IT team to say, hey, we're scared to roll this out because we just we're we're scared somebody's gonna gonna make a mistake and then we have to self report on the on the compliance issues and stuff like that. So we kept it very small, A very small core group of us with those guardrails that we put in place, we used it for, I'll say, a few months, probably three months before you rolled it out to anyone else. There were three of us in there primarily using it every day. And so in that, we kinda trained ourselves and I'm still learning because GovDash changes constantly for the better. But it's the improvements are are coming through and the upgrades are coming through that we we kinda see your road map through the the platforms. So those on the call that can't that aren't current customers. Within GovDash, you can see the road map that GovDash is developing, and so you can see what's on the horizon, and you can vote for it and see what's coming through. And so everything that we vote for, we see it coming, so we're eagerly awaiting updates every time they come out to make it even better. But we use the the three core people in our group to to kinda try to break it. And I think we succeeded a couple times in the initial early days of GovDash to break it, and now it's kind of bulletproof. I haven't been able to break it in a while now. And so we would throw everything we could at it just to confuse it, to break it, to figure out, hey, what are the cons on this? What can we not do with it? But the brain behind it, the LM or however it switches between models, I can't break it anymore. And so the way that I train our our internal team of three was using GovDash to train us because the the bot inside, it has all of your troubleshooting documents, has all of your SOPs and stuff built into it. So any question I have, I can create a prompt that helps me train myself knowing what I currently know about GovDash. And so for the new employees that we brought in on it, I changed that prompt to someone who is brand new to Govdash, didn't know anything about it, have that that bot within GovDash now help them learn how to use GovDash, and it's built into the the same platform. And so we've also got, Diane. Diane is part of our our team from GovDash. She's she's our our representative. And internally, I would say weekly, she's meeting with at least one of us from the team to create custom documents for us, to create custom templates, do internal training for some of our our new members to do onboarding and so forth. Whether it's a a capture team member or proposal team member or an operations team member, she she's got the the full gamut. She's able to get everybody on point up to speed, so it makes my life a lot easier. Having someone from GovDash assigned to us to kinda do that internal, onboarding and initial training to get it there. So she's great with that, but it's the initial team back to the point was this this core three people to make sure, yes, we're we're scared about this and we don't know how it's gonna work. We just wanna make sure that we do this right. We don't wanna make a mistake and deal with the the wrong company or just get too too flexible with it and too relaxed and end up having to self report make a mistake. And so after we there's three people got in there and understood it completely, then we started rolling it out to just our proposal team. So then it was proposals that were not CUI, not classified, just commercial customers or just small, full and open opportunities we would go after, understand how it builds our past performance, how it understands our previous contracts and so forth to build the past performance and to build the outline and then dig even deeper to take all the capture questions that we've loaded in there and create that true outline so that we now go from discovery to target to capture straight in the proposal management. And then, essentially, the first draft that we see is pretty much red team ready, going back to the to the shipping method. And so that just the return on investment there is just invaluable. And so proposals that used to take us several months to do are now taking us days to a week or if we can have to to complete. And so it was a it was a slow process just for our own internal control. We wanna make sure that we we did this right and didn't just release it to the world, and now everybody's throwing every document in there and and not understanding the guardrails that we put in place. And so, I think that's the answer. It's just to do a smart start small, get a core team, create. We've got an internal AI review board that I'm on, and we review all AI platforms that come out to see what do we wanna implement, what are the risks, what are the returns on this. It's not just GovDash. It's it's all other types of softwares that that come out as well. And so we wanna stay on the forefront to identify that stuff to make sure, hey. If this is a benefit and it can help us in industry and stay compliant, let's go after it or let's at least do a demo and see how this is gonna work for us. And so I think kind of the core team internally that's focused on AI that can stay on the forefront of the AI news because it does change every day and have that team kinda advise your your c suite and advise your IT team have at least one number of your IT team on that board, but be able to advise both sides because you've you've been talking, you've been deep in the weeds, understanding and learning about all these different platforms first. That was key for us. Yeah. I I totally agree. I think being, first, curious and then also flexible because, just how quickly the world is moving right now, and these technologies are are advancing is is, it's a necessary thing if we're if we're all gonna stay up to date with what's going on. You you mentioned the, the review board there. I'm curious. Across the organization, maybe even beyond business development, where else are you seeing AI make an impact for, for Alberts? So AI for us, we use it a lot on the engineering side. A lot of the sensors we create has AI built into it, and that's way above my my level. But our our finance team is starting to look at it. We're we're still scared just because, again, the the cloud is a is a scary thing where it goes, where the server actually is is housed, has access to all that, so the financial information in there is is scary. But the way that we've we've been using it is is called this Vibe Coding. I don't know if you've heard of that, but they've been using Vibe Coding to create kind of price to win tools. And so a place that our finance people can can put in our labor rates, our g and a, our our profit rates and so forth, and it kind of creates a kind of a a a template and matrix that our BD and capture teams can use to build these price to wins and build quotes and so forth baked in with our current rates. And the master version of that is kinda owned by our our IT and our finance team. So we always know those rates are are accurate and current. So depending on the on the country or the the type of business that we're doing, the service industry, the labor rates change significantly and then travel and overseas, Okonos travel changes and per diems and so forth. So we're able to kind of build those those platforms customized to hours to have all those fields that we fill in. So they've been able to use AI to to build those functions. Wow. And and on the engineering side, I'm I'm interested in, so as part of the biocoding is that using, these these AI coding assistance slash agents, just for helping to spin up prototypes. And then from there, maybe kind of hammering out all the issues and turning into more of a production ready thing. Is that is that usually the flow? Yeah. It it's usually well, I would say usually. It's it's not a usual thing for us. This is kind of a a one off that we we just finished up this past week was the Bristowind tool. And so that that's new for us. That's a learning curve. Just figure out, hey. Let's let's throw some some sample, quote sheets at this, sample price to lens, and so forth to figure out where are we, how does this fit, and does it does it make sense to to keep going forward. So, again, this is their their first week using it, and we're getting rave reviews on it. We just need to kinda there's a few customizations that that certain business units want. So we wanna make sure that it fits all business units, or do we need to create an overall hours and break it out by individual business unit, so they have their own custom model? So that that's currently where we are there. Understood. That's really cool. So, Greg, I'd love to, I think we've hit on some really important, points here, and you've gone into significant detail on all of them. I think it would also be really useful just because, like, personally, it's easy it's easier for me when I've got sort of the greatest hit hits list. If you had, like, maybe a core set of steps, when it comes to evaluating AI, like an AI evaluation playbook, what would you include that you think every team should should be factoring in in some way? Yeah. I think the biggest thing is defining what you what you have to have in in your system versus your your nice to haves versus need to haves. I would prioritize the need to have first. Look at industry, figure out where AI is being used. Look at the government, figure out where they're investing money in AI, where where that's going so that you kinda match that in the lockstep, because that's that's rapidly growing. They've invested billions in into implementing AI in the on the government side. So ensuring that you're trying to keep up with with that because you don't wanna be left behind. And in our company, a lot of people initially were saying we're scared that AI is gonna replace people. And it's it it may at a granular level at very small levels, but people who understand how to leverage AI might replace people. Businesses who understand how to leverage AI might now push to to the top. So that's where we're we're trying to be. We wanna understand the landscape and be able to make that intelligent decision based on information that we currently know because we're doing the research and because we're on top of the the AI news every day to make sure we we're aggressive with what's coming out. I think identifying those need to haves is is good and then kinda throw the nice to haves kinda on the side and see if there's any way that they can coexist together. And then the the next piece would be the compliance piece of it, obviously, for GovCon. Commercial, not so much, but GovCon for sure. All the requirements, everybody knows, CMMC is is coming up. You've got this, you've got FedRAMP. There's all these different requirements that you have to maintain. And so making sure that the platform you go with understands those requirements and it's not just like I said before, it's not just AI engineers trying to create a platform that's GovCon compliant, it's GovCon people using AI engineers to make that platform because they fully understand and know what GovCon is is requiring in the space. And so that was big for us and the primary reason we we chose GovDash because it felt like GovCon professionals built this with AI engineering support. So that was the biggest thing that kinda got through the hurdle, the buy in through the business units and through IT. And then the flexibility piece of it, like we discussed, adding in that extra requirement for us to kinda put the blocker up for CUI and classified information to make sure that people don't accidentally open the document that AI is it has visual on. It can't see anything until you tell it to open it, and then you actually confirm, does this document contain CUI or classified information? So that brings an an element into it instead of just this all knowing eye in the corner that kinda sees everything you open up on your desktop. That was a huge concern for us. But the the IT buy in was was huge. Once we had their buy in, it was pretty much smooth sailing. Getting the the BD and operation to buy in was was pretty easy just because they understood this. Like I said before, we're running fast with scissors trying not to drip, and so everybody's wearing multiple hats, and they understood this could help us out. This could this could help us wear those multiple hats, but do it efficiently and be able to take tactical decisions and not just throw spaghetti at the ball to see what sticks. We can actually take a step back, focus on this, and create a targeted approach with a true black hat or true capture strategy that should go after something instead of just, hey. We got two weeks to respond to it. Let's just respond, throw stuff on paper and see if it works. We don't wanna do that. So it's more about the quality over quantity and that's where initially the small business, you just go after everything you can. Just quantity, respond to everything. Now we're trying to scale it down and go after quantity, and that's where AI is very capable of of leveraging and and making that that true decision. But the the demos and the pilots were huge for us just to be able to see all the different platforms, and they they varied significantly. A lot of these that said they were the the turnkey solution and tip of the spear and all that, I knew immediately. And I think we talked about this before in the past, but I knew immediately they weren't the answer because AI changes every day. No one can say that their turnkey, tip of the spear, anything like that because it changes so fast. So for someone just transparently say, hey. We're we're doing the best that we can. This is our best version of our platform at this moment. Please give us feedback so we can make it better. That that was key for us. And DoorDash has been very, very customizable, which to me was a refresher. I'm not used to having that type of responsiveness, coming from tools like this. It's usually, hey. Sorry. This is just how we how we have it. There are some workarounds, but you just gotta work around it. That that's not how it's been here. Everything's been customized to fit exactly what what we need, and it it's been it's been great. Well, we really appreciated all your suggestions. They have, they've been truly great. Also, I think so far, this has been fantastic. I do wanna make sure that we answer any questions from the audience, as well. So if you do have questions, feel free to throw them in the chat here, for for Greg and myself. I actually had a a few questions maybe just to get us kicked off here, though. I'm really curious. What has been the most surprising, outcome so far from adopting AI in your organization? Yeah. So the biggest thing for us and the thing that I always wanted to push when I started and just for some context, when I started at Albers, I was the only proposal person. They didn't have a proposal manager. It was business development professionals kinda doing their own proposals kinda ad hoc on the side. So I came in just a a one person show. In doing so, it took a lot to kind of write this SOPs, establish, hey, this is our actual proposal shop. This is how we're gonna integrate with the systems. This is how everyone works together and integrate as a team. And moving on from there, we wanna build a true business operations unit, so that's what came in next. That's kinda where AI came into the picture was after it already established that proposal shop. We moved into proposal operations. So from there, I'll get as far as alright. Now our BD and capture teams, they need some leverage on their side, but we don't have the bandwidth and the resources to create a true business intelligence unit. Leveraging AI, I've been able to create a very comprehensive business intelligence unit with just one person. And so this one person is able to do deep dives using GovDash, which wasn't even part of the package when we first started. I just kinda I just kinda learn that feature as we're using them. Just the thought was, I wonder if it can do x. And so I just started throwing significant prompts out of they're usually two or three pages long, and it would they would give a very strategic pinpoint answer. And so the light bulb came on, I was like, alright. We can actually create this business intelligence unit by using GovDash. So we create these these custom prompts with our one person, get her trained up on on how to do this. Now she works with our business development teams prior to even coming into the proposal phase. She's on discovery target phase, and I look at, hey. Who are your customers? Do you know your your incumbent? Do you know the competitors? What's the landscape here? If they don't know that, she can go out and grab it. So they're even able to go to conferences and have their meetings and just walk before and then come back and give that information to her And she can do a deep dive on everyone that they've met, kinda give a synopsis of of what's what in that that landscape for that proposal. So creating that unit was the the big surprising function for me. And even our executive leadership team, when they are our c suite, when they're looking at m and a's, they wanna do due diligence reports on on companies that we're looking to to purchase and and acquire. They're able to pull those reports and the the AI can do a deep dive inside of USA spending and pull their financial information and pull their board of directors and their c suite, see if there's any, conflicts of interest that we need to be aware of or any negative news in the press and stuff like that. So it's able to scrub all those things, which the more we use this, just the more ideas come up as far as, hey, where else can we use this? And so that's still organic and growing with GovDash. But for right now, the two primary functions are BD slash proposal and capture slash, business intelligence. So once we get that fully encompassed and trained up internally, we're just gonna start pushing that company wide to to get this pushed far and wide. Awesome. Yeah. Those use cases are super interesting. I I think that's also a, a pattern that we've been seeing, especially over the last I put I posted about this a little bit, especially over the last, like, quarter or so, on the system. I'm also really curious. So right now, we're seeing, I think especially as people anticipate more more solicitations to drop, there's a bit of urgency with some companies that have not yet adopted AI, where they're trying to figure out, or or they're worth they're trying to adopt it as as soon as possible. But also, they don't want to skip critical pieces of due diligence. Do you have any advice to teams like that where, again, like, they wanna bring something in house as soon as possible. They wanna get their team trained up and spun up. Do you have any thoughts on, like, what are the core pieces that you shouldn't skip over even if you are in a rush? Yeah. I think again, I don't I don't work for GovDash. I'm not gonna say it's it's the only one out there because there's tons of other ones out there, which which we did our our t and e's. But I would encourage everyone to do demos of multiple platforms to include GovDash. I say that because when we did our our demos, again, they all felt like they were created by AI engineers to do b d, GovCon type work. It was a very easy choice and it stood out instantly when we started using GovDash to see the life cycle to understand the functions and kind of the behind the scenes prompts, which I haven't read your prompts. But any question we ask or any insert we put in for the capture questions, seeing how it spits that out in our our future outline, Understand there's not just one prompt. There there's multiple prompts that go in there and it deep dives. They build on each other and so it's kind of building the story in the background. I don't know how you do it or how it works, but I was extremely impressed by it and that's why the outcome I got, it didn't feel like just AI generated language. It didn't feel like the hallucinations you can get by using OpenAI or chat gbt. It was very pinpoint structured specifically to what we were asking for it. Even to the sense that we're able to as a user of GoDaddy, we can throw in some custom instructions and so I have it put in parentheses needs answer if it doesn't have an answer for something so that it will not hallucinate and then we we accidentally get false information, get plugged into our our outline and so forth. So it follows those instructions, but it understands our past performance. It understands all of our internal documents we've thrown in there to truly understand who our company is and write the content based on that, not just generic. Hey, if you were company x responding to this RFP, this is what you should should propose. It truly understands what who we are and what we do. So I think the biggest step is doing those demos to include GovDash so you see the landscape and the dashboards and the layout life cycle of all the others. And those demos are typically a week to two weeks each that you can do multiple at at a time. But, for sure the demo is the stuff that you don't wanna skip. And then once that's done, once you have the buy in, you understand the return on investment, then go to the ice IT compliance side. Ask for those internal documents. What are your current certifications? What's your current road map to be FedRAMP or NIST or c CMMC? Where's what's your current landscape in that area so that now you've got the buy in from operational BD side. Now you've got enough information to at least start the conversation on the IT buy in side, to give them the foundation of what that platform has, but the ability to ask questions and kinda customize their answers after that. So those are the two main steps I would definitely not skip. Got it. I think that's really important. Alright, Greg. I have one final question for you. I'm, I'm I'm curious to to see how you're thinking about this. What are you most excited about, let's say, over the next year in terms of what AI can do for you in the in the business? Or maybe even just generally as, these technologies again are, like, advancing so rapidly. Yeah. So the the big thing I've I've been playing with, and I'm still very, very shallow in-depth on it, is the the AI agents, using these to automate workflow. I've got a couple other platforms I use that are kinda infused AI, not as much as GovDash, but they're they are infused. And being able to receive, for example, a request from a business development manager who's at a conference, a request for business intelligence report on a company he just met, they can submit that on their phone through a Google form or whatever. It goes through Zapier and it creates the automation through the agent. It does that deep dive using the the the prompts that we created with GovDash to create that comprehensive BI report that now that person on the floor has full access to a report and is automated. It didn't include a single person that process to do that. So currently working on that. It it's a little difficult because I'm not an IT guy. I'm not an AI guy. So learning the different prompts and and knowing how to pull code and all that is a little bit different, but now this Vibe coding makes it a little bit easier for me. But there's still a learning curve there. So that's what I'm excited for to see where that can go. I don't wanna replace people, but I wanna kinda force multiply. I wanna still have those people manage those teams of agents, if you will, and create that just that flow of information, that immediate flow of information. So when you're on the floor in a meeting, you you send that one request and that agent understands who you are, where you are, why you're asking the question, finds information, puts context around it, and then spits it back out to you. To me, that's invaluable. And so that's currently what we're working on. I'm trying to or what I'm working on, just kinda a side project of mine to kinda figure out how that would work just in my role. And then once I figured out, to push it out to the rest of the team and show them, hey. This is what can be done with it if we invest. Yeah. And, we're that that is a big area of development for us at, internally at GovDash as well. So, as always, Greg, you give us your thoughts, and we'll try to we'll try to bake that into the system. Yeah. Absolutely. Well, I just wanna say thank you so much, for doing this. Everybody, Greg James, I think, has just run us through a very, a very helpful guide and tactical guide for evaluating these AI systems, in business development, but also across the board. If you have any other questions for us, please let us know. Appreciate you all for attending today, and hope you have a great rest of the day.