The AI technology that eliminates knowledge bottlenecks

We've all dreamed of it: feeding our AI all our business knowledge and just asking questions. It felt like science fiction—until now. RAG databases are the breakthrough technology that finally makes institutional knowledge accessible to your entire team in seconds.

In this episode, Micah and Beau dive deep into Retrieval Augmented Generation—and yes, they acknowledge it's probably the worst-named technology ever invented. But beneath that terrible acronym lies the solution to one of business's most persistent problems: knowledge bottlenecks.

Here's what makes RAG different: instead of training new team members for months, they can access every solution you've ever created by week two. Instead of your sales team saying "let me get back to you" on technical questions, they have instant answers. Instead of routing every complex question to the same three senior people, anyone on your team can query your entire knowledge base.

RAG works by taking your existing documents—from Google Drive, SharePoint, or any cloud storage—and making them instantly searchable through AI. The technology breaks down your content into searchable chunks, creates relationships between information, and serves up exactly what your team needs when they need it.

In this episode, you'll hear:

• Why RAG databases are the missing piece in most business AI strategies
• How to sync your existing cloud storage directly to an AI-friendly database
• Real examples of eliminating knowledge bottlenecks in customer service, sales, and training
• The step-by-step setup process for implementing RAG in your business
• Why this technology changes everything about team onboarding and productivity

This isn't just another AI trend—it's the technology that transforms how your team accesses and uses company knowledge. If you've ever felt like critical information is locked in someone's head, this episode is your roadmap to freedom.

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For more information, visit our website at biggestgoal.ai.

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Transcript

0:00:00 - (Alane): Welcome to Automate Your Agency. Every week we bring you expert insights, practical tips, and success stories that will help you streamline your business operations and boost your growth. Let's get started on your journey to more efficient and scalable operations.

0:00:18 - (Micah): All right, Beau, it's me and you today. Alane is out on adventures. So we're going to take over the podcast. We're going to talk about retrieval augmented generation. Super exciting.

0:00:31 - (Beau): Yeah, I'm super excited about it. Let's do this.

0:00:34 - (Micah): I'm sure everybody that just heard me say that knows exactly what I'm talking about because it's clearly the best name technology we've ever invented as humans.

0:00:43 - (Beau): It really is. Honestly. I'd probably say it's probably one of the worst names that we've named anything.

0:00:48 - (Micah): It is. I hate it. Do you know what the acronym is? RAG.

0:00:51 - (Beau): It's one. Yeah, that's pretty.

0:00:53 - (Micah): It's RAG. It's tough. We gotta go into meetings and we gotta talk about rag.

0:00:58 - (Beau): Yep. I do like the fact that we have put into the meeting slides that it is a terrible name. So at least we get that out of the way and we know we didn't name it, right?

0:01:06 - (Micah): That's right. Yeah. We're trying to take no ownership of this whatsoever. Naming aside, I will say this is one of my favorite technologies we've been able to leverage maybe ever.

0:01:17 - (Beau): Agreed. So this is something I love talking about. And, Micah, I think it might be really cool for us to just talk about how we found our way to it and kind of those use cases, what it does, how it's set up and all of that.

0:01:28 - (Micah): Absolutely, absolutely. So, you know, we've been even on this podcast, and both for our clients, we've been doing a ton with automation. Then we added AI to the automation. Then we get into agents. And one of the things that's been difficult, I would say, or maybe even just missing this whole time, is how do we take business knowledge? So I remember a couple years ago when we first started getting into AI, we would have requests, and obviously we would have the same dream. Why can't I just take all my business information, feed it to an AI, and then ask a bunch of questions?

0:02:04 - (Beau): Right.

0:02:04 - (Micah): And that was pretty much science fiction a couple years ago. But today we're really, really close and it's using RAG databases to do this. So essentially what it allows us to do with. I'm just dumbing it down and not trying to use a bunch of technical jibber jabber here, which is we're going to take A bunch of business information, documents, assets files, spreadsheets, whatever you would want to have available.

0:02:35 - (Micah): And you put that into an AI friendly database. Once you do that, you can add an agent that can access one or more of those databases and leverage that information with its own training data.

0:02:48 - (Beau): That's amazing. It's so amazing to see that in action. And as far as the setup of that, what does that kind of look like? I'd love to just kind of hear you talk about that portion of it. I mean, obviously you can put it into that AI friendly database, but how do you get it there without it taking a big lift?

0:03:07 - (Micah): Yeah. You're going to make me get technical, aren't you? I am.

0:03:10 - (Beau): I was going to make you get technical into it.

0:03:12 - (Micah): So I would say prior to maybe six months ago, you had to do stuff that was take a document. Well, let me back up, let me just say what's happening. So instead of a relational database like we're all used to, even if you don't know what it's called, that's your standard database type for RAG databases or AI friendly databases. We should just say AI friendly databases.

0:03:38 - (Beau): Yeah, I think that's the easiest thing.

0:03:39 - (Micah): Yeah, yeah, much better.

0:03:41 - (Beau): Yep.

0:03:43 - (Micah): What happens is you would take say like a PDF, so if you had a PDF that you wanted to put in a database, what you would need to do is break that down into a bunch of separate text chunks, like it might be a thousand character text chunks from that PDF and then do what's called creating beddings. And then you put that into a vector database. And that vector database is creating relationships between all those text chunks that essentially runs an algorithm and says, how similar are these text chunks? And that is going to be your AI friendly database.

0:04:17 - (Micah): And what happens is when a query comes into that database, it runs a similar equation or algorithm and says, well, what are the most relevant text chunks to that query? And it pulls those out of the database. So instead if you think if you have a hundred documents, right. A regular AI chat or a regular AI project is not going to be able to handle all that information in a cohesive way without hallucinating.

0:04:43 - (Micah): It's too much, too much, too much, too much for anybody to handle. Humans, really.

0:04:47 - (Beau): Yeah.

0:04:48 - (Micah): So you put those hundred documents, you embed them into this vector database, and when the AI makes a query to that database, it's only pulling out the relevant amount. So instead of getting everything, you're just getting the segments or the sections that are most relevant to whatever the query is at that point, which Ensures that if you have a context window that looks like this or is a larger context window, this is only taking up a small percentage of the context window. So you still have room for processing, you still have room for chats, you still have room for discussion.

0:05:25 - (Micah): Whether you're doing it with the AI or it's an AI agent that's needing to process multiple things, you always have to have room for the context window. What's really cool is it can do that multiple times, so it might pull out some relevant information and ask itself, is this what the user is looking for? I don't know. I think so. Let me do another query and see what else we can get. And so all of that was my very long winded answer to your question.

0:05:54 - (Micah): Yeah, but that works really well. The problem that we've had is how do you keep that database in sync with the ever changing information in your business?

0:06:06 - (Beau): That is the difficult part. And I think what's cool about this is, you know, to that categorization piece and talking about how you can break things down, you do still have the ability to go in and actually build in those different levels of like partitions, things like that within the database. To say, this is a category of information, this is category information. And to your point, where you're going with this is having the ability to then always consistently have that syncing with that AI friendly database.

0:06:32 - (Micah): Absolutely. So tools are coming out now. It used to be, hey, how do we easily set up a vector database? All right, we got that. But then we still have the syncing problem. Now we have tools that are coming out or are out and becoming more and more powerful where essentially it's allowing you to manage your documents and your files just like you do currently. Microsoft, SharePoint, Google Drive, Dropbox, all of those. You leverage your cloud storage to save and organize your company files and documents and assets.

0:07:08 - (Micah): But then like you were saying, bo, you could connect certain folders into a vector database now and that database is watching those folders. So every time you add, edit or remove, it syncs up and keeps the database completely in sync with how you're actually managing your files and your folders in your company. Which is mind blowing.

0:07:34 - (Beau): It blows my mind it took us that long to get to this point.

0:07:37 - (Micah): That's super cool. Super. And that opens the door for so many things with AI in a business. Because today, right now, you could say, hey, I'd love to have an AI agent that takes a one proposal, rips out all the, you know, client information and writes a use case on what we did, that makes it a generic solution and then adds that to a folder in Google Drive. And then we sync that folder from Google Drive into an AI friendly database and then we have a second agent that can access that database. And anytime we need to write a proposal, we have it go to that database of all of our completed projects or won proposals and helps us write the proposal based on successfully, successfully completed proposals that we've done in the past.

0:08:36 - (Micah): And that's just one example.

0:08:38 - (Beau): I know, that just amazes me, the implications though on that. I mean, if we take that a step further and we talk about some of those use cases even further than that. I mean, for me, if I think about anytime we hire someone new, a lot of times we're hiring for those, not necessarily always soft skills, but the way that they're leaning towards a particular skill set. But then really what takes the longest is our institutional knowledge or the knowledge on our products, the knowledge on our services.

0:09:03 - (Beau): But now with something like this, we can get that information out of our heads into a database that. Now I could probably, second or third week, the persons on my team, if I'm on a customer service side of things, I can put them on a phone call and say, hey, do what you do best. Here's something you can access and you can chat with that information. Right? So you can set up a chat to actually be able to chat back and forth with it.

0:09:29 - (Micah): Yeah, I mean what you're, what you're talking about is a great use case. And I guess I didn't mention earlier, when an agent is, and I should say AI agent, not a human agent. When an AI agent is accessing this database that can be triggered from a chat interface. So any of us or anybody on your team could start chatting with this huge set of data or multiple sets of data without having to know what page, which document, where do I find this, etc. The AI agent and the, and the AI friendly database is going to take care of all that. So in your example, correct me if I'm wrong, but what you're saying is if I was the brand new customer service person, I could have a chat interface that would allow me to ask any questions. Let's say it was a huge database of product specifications.

0:10:21 - (Micah): I could actually go in and ask questions about any product that we have, and that AI agent could go to the database and in seconds find me answers, return it to me. Even while I'm responding to an email or on a call, answering questions or on a presentation, I could just be like, hey, hang on one second, let me look that up, type it in, it gets the answer, it responds back a hundred percent.

0:10:46 - (Beau): That, I mean, even extending to technical support, extending to sales support, anything like that, I mean, before you know, if you think about sales, you get a technical question on a sales call and you're a non technical salesperson, what are you going to say? Well, let me find out for you. That is a deal killer.

0:11:00 - (Micah): Let me get back to you.

0:11:01 - (Beau): Yeah, let me get back to you. You're killing that deal at that point in time.

0:11:05 - (Micah): Right.

0:11:05 - (Beau): So just being able to have access to all that information, type it in, ask your question and to be able to have an agent go get it for you and bring it back to you and you can immediately on the call provide the insight and that's huge. That is leaps and bounds from, from where we've been. That's really, really huge.

0:11:22 - (Micah): I mean, what we're really talking about here is eliminating that bottleneck that's so common in so many businesses. One or two or maybe three people depending on the team size, has immense amounts of technical and kind of that institutional knowledge or that company knowledge, or that product knowledge or that client knowledge. And everything has to be routed to those few people to do things like produce a quote, produce a proposal, answer questions, get solutions.

0:11:55 - (Micah): Whatever type of business you're in, whatever you're looking at, those are huge roadblocks and huge bottlenecks that we see time and time again that with the right AI friendly database setup and with the right either workflows or chat interface, you could automate this or provide access to that huge amount of data in seconds.

0:12:17 - (Beau): Absolutely. I think you guys had a previous episode where you talked about email triage, right. And I think you touched on how this could be utilized in email triage. But you know, that's a great use case as well. Being able to have an email come in, an AI agent watching and that inbox reading what's happening in that email that's coming in and from there being able to go and retrieve a response and put it in there. So the human, all they're having to do is look at a draft email and say that's the correct response and respond back.

0:12:46 - (Beau): And I mean that, it just amazes me.

0:12:48 - (Micah): Yeah. So I think for the most part that was the main thing that we wanted to convey and bring to our listeners today, which is this technology exists. You can manage your files as you are now, you can sync one or more databases as an AI friendly database. You can have automated routines or automated workflows that have access to those databases. Or you can simultaneously it should be and or you could have chat interfaces that allows anyone on your team to also be able to access any of these.

0:13:26 - (Micah): It's incredible. It changes how you do training. It changes how your field teams could work, it changes how you could potentially solution, how you create proposals and contracts, and how you handle incoming email or support ticket or client questions. Touches every aspect of a business when you start brainstorming and thinking about all the different places this could be used.

0:13:52 - (Alane): Thanks for listening to this episode of Automate your Agency. We hope you're inspired to take your business to the next level. Don't forget to subscribe on your favorite podcast platform and leave us a review. Your feedback helps us improve and reach more listeners. If you're looking for more resources, visit our website at biggestgoal AI for free content and tools for automating your business. Join us next week as we dive into more ways to automate and scale your business.

0:14:17 - (Alane): Bye for now.