Bill Rogers: AI-Powered Assistants, Chat, and Search for Content Platforms – Episode 38

Bill Rogers Bill Rogers is an experienced AI entrepreneur whose latest venture, ai12z, gives web content platform owners tools to build digital assistants and chatbots and to run gen-AI-powered searches. We talked about: his work at his latest startup, ai12z, which builds copilots designed to power content experiences his use of the term "copilot" as a generic AI capability, to distinguish it from branded uses of the word the two main capabilities of their copilot: question answering and ReAct (reasoning and action) his take on RAG architectures and how ReAct fits into them how integrating copilots into content and commerce architectures can guide users through complex interaction flows that are connected to third-party services how to ensure that users have confidence in AI systems and that the systems are technically secure the technical architecture that underlies their copilot platform how copilots help write queries to search utilities and other information and knowledge sources to help with tasks like complex product comparisons the variety of UIs their platform provides: search boxes, knowledge panels, etc. how interactions with copilots can inform an organization's content planning the importance of including image AI in this kind of platform, to both better understand the content and create more robust ALT text Bill's bio Bill Rogers is a visionary entrepreneur with a deep technologist background in AI and digital technologies. Recognized for significantly influencing the evolution of online experiences, Bill founded Ektron and served as its CEO. Under his leadership, Ektron emerged as a pioneering SaaS web content management platform, serving thousands of organizations globally. After Bill sold Ektron to Accel KKR, it merged with Episerver and became part of Optimizely. Bill then co-founded and led Orbita as its CEO, driving innovation in advanced conversational AI. Beyond these startups, Bill co-founded several other ventures and has had an expansive career in digital signal processing and robotics engineering. Bill holds a Bachelor of Science in Electrical Engineering from Boston University. Connect with Bill online ai12z bill at ai12z dot com Video Here’s the video version of our conversation: https://youtu.be/hJPnAvWXBlA Podcast intro transcript This is the Content and AI podcast, episode number 38. You wouldn't try to operate an airliner without a copilot, and you shouldn't operate a modern web architecture one function at a time either. That's the case that Bill Rogers makes for his latest AI startup, ai12z. His company builds AI copilots - in the generic, non-branded sense of that term - that enable robust search and discovery, streamline complex tasks like mulitfaceted product comparisons, improve accessibility, and even help with content planning. Interview transcript Larry: Hi, everyone. Welcome to episode number 38 of the Content and AI podcast. I am really delighted today to welcome to the show Bill Rogers. Bill is a longtime veteran in the content management and technology world. He founded a company called Ektron years ago, which was acquired by Episerver, which is now known as Optimizely. He ran a conversational AI platform long before ChatGPT came out called Orbita, and he's currently the CEO and founder at ai12z. So, welcome, Bill. Tell the folks a little bit more about what you're up to these days. Bill: Thank you, Larry. Yes. So, at ai12z, what we're doing is we're focused on building essentially a copilot, enabling websites and mobile applications, the ability to take advantage of AI to help drive experiences. Larry: Nice. And that's a nice, succinct description of what you do, but a lot of websites have chatbots or things like that. How does a copilot... Well actually, first let me back up because copilot is an interesting term. I first became aware of it when GitHub did their coding assistant thing, and then Microsoft has a whole suite of branded products called Copilots. But we're talking about a generic capability. Is that correct? Bill: That's correct. I think the term copilot, Microsoft has used quite a bit, but it is a generic term. We actually like to refer to it as a website AI assistant. And if you think about it, in the days of Ektron, we had this phrase, "What do you want your website to do?" And now we are talking about, "What do you want your AI to do for your website?" Larry: Interesting. Human needs haven't changed that much, but we have all these new capabilities. I guess what are one or two use cases that have jumped out early in your journey that are really helping people? Bill: So, when you think about, "What does copilot need to do?" So, one of the obvious things is this ability to be able to answer questions. And so when you talk about years back, when people were building chatbots, the challenge was creating the knowledge for that question and answering took a tremendous amount of work because you'd have to curate each piece of content that you're going to answer a question with. You had to create an intent model. Just an awful lot of work. Bill: Today, we have a CMS connector, we ingest the data and we can answer any question that your content actually have. You don't have to redo anything with your content in order to make it usable for question and answering. So, that's the first step, just question and answer. Bill: Then there's this concept of ReAct, which is reasoning and action. You enable these agents to do things. It can talk to backend systems like CRM systems or it could talk to any system that you have in your system. You just make a REST API available for it, and all of a sudden we can now use this data to create workflow to accomplish tasks that used to take an awful long time to go do and create, and it doesn't need to be that way at all anymore. Larry: Yeah, I know a lot of conversational designers and I've watched them work in Voiceflow and tools like that and hand crafting all those query... all the questions and answers basically, and the intent discernment stuff that they do. There's a lot to that. And so that ReAct, that sounds like a really intriguing... it's like you can get your fingers into any other system that you have. And this kind of reminds me of a... Is this in the family of a RAG architecture where you're... Bill: So, a RAG architecture would actually be just an agent to a ReAct system. So, let's just describe RAG. To the users, RAG is a way for you to, instead of using the knowledge of the LLM, you are using the content of your own content and you're answering... the LLM is answering questions based on that content. So, you have typically a vector database that when you ask the question, it gets the content and based on the content that it gets, the LLM will analyze that content and build a summary answer to it, actually very, very robust. And so that's a core piece to it. Bill: What ReAct does is that there's a large language model that does the reasoning. It thinks about what came in as a question and says, "Can I just answer that question or do I call one of my agents to help me answer the question?" And so, one of those agents can be ReAct... I mean, can be the RAG. Bill: So, why that becomes very exciting is that let's say that you want to compare two products. Your RAG has the information about each product in the system. The reasoning engine knows if you said... We'll use an example, sports example. If I said I wanted to compare the stats of Bobby Orr and Derek Sanderson, that's very tough for RAG because that one compare question, are you going to find content in your system that actually does do the comparison? And you're likely not. And so what will happen is that the reasoning engine says, "I'm going to go call the RAG for Bobby Orr, and then I'm going to call the RAG for what's the stats of Derek Sanderson." Bill: It gets the answer of those two information and then it combines the answers to do the comparison, and you get an amazing comparison around that concept. So then you take that step to the next level with a reasoning engine. And the reasoning engine, you tell them about all the tools that you have available to it: email, SMS, CRM, and the list goes on. Google Maps, Google Places. And you then say something to it like, let's say you're a hotel and you said, "What is the directions to the hotel from the airport?" Bill: And so the reasoning engine, from its system prompt, knows the address of where the hotel is and it knows where the nearest airport is, and it'll actually call an agent called Google Maps and it passes to that, the address of the airport, address of the hotel and IT generates the Google map with the full map and the link so that you can actually... so you see all the directions just like you would in Google Maps, but you can click on it and now it's in your mobile phone. Bill: So, you can see how a hotel can start looking at a reasoning engine as enabling all these third party services. Like if you said, "What are my activities?" Then the system is intelligent enough to say, "Oh, I have these eight activities, would you like to learn more?" And it gives you call to actions to learn more. And you then click on learn more and you see something about golf that you were interested in. It tells you about golf and you said, "Would you like to book a tee time?" You click book a tee time, a form has to come up to collect who are you and it collects your first and last name, your room number. And then it says, "Do you want to pick a date and a time?" So the time slots, when you pick a date, the slots are going to change. So now you pick all the information and then it might say, "Do you want to rent a club car?" Bill: And then it collects that data and it'll analyze it, send you an email, register it with the system of record that this booking has occurred.