Andrea Volpini: The Role of Memory in Digital Branding for AI – Episode 27

Andrea Volpini Your organization's brand is what people say about you after you've left the room. It's the memories you create that determine how people think about you later. Andrea Volpini says that the same dynamic applies in marketing to AI systems. Modern brand managers, he argues, need to understand how both human and machine memory work and then use that knowledge to create digital memories that align with how AI systems understand the world. We talked about: his work as CEO at WordLift, a company that builds knowledge graphs to help companies automate SEO and other marketing activities a recent experiment he did during a talk at an AI conference that illustrates the ability of applications like Grok and ChatGPT to build and share information in real time the role of memory in marketing to current AI architectures his discovery of how the agentic approach he was taking to automating marketing tasks was actually creating valuable context for AI systems the mechanisms of memory in AI systems and an analogy to human short- and long-term memory the similarities he sees in how the human neocortex forms memories and how the knowledge about memory is represented in AI systems his practice of representing entities as both triples and vectors in his knowledge graph how he leverages his understanding of the differences in AI models in his work the different types of memory frameworks to account for in both the consumption and creation of AI systems: semantic, episodic, and procedural his new way of thinking about marketing: as a memory-creation process the shift in focus that he thinks marketers need to make, "creating good memories for AI in order to protect their brand values" Andrea's bio Andrea Volpini is the CEO of WordLift and co-founder of Insideout10. With 25 years of experience in semantic web technologies, SEO, and artificial intelligence, he specializes in marketing strategies. He is a regular speaker at international conferences, including SXSW, TNW Conference, BrightonSEO, The Knowledge Graph Conference, G50, Connected Data and AI Festival. Andrea has contributed to industry publications, including the Web Almanac by HTTP Archive. In 2013, he co-founded RedLink GmbH, a commercial spin-off focused on semantic content enrichment, natural language processing, and information extraction. Connect with Andrea online LinkedIn X Bluesky WordLift Video Here’s the video version of our conversation: https://youtu.be/do-Y7w47CZc Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 27. Some experts describe the marketing concept of branding as, What people say about you after you’ve left the room. It's the memories they form of your company that define your brand. Andrea Volpini sees this same dynamic unfolding as companies turn their attention to AI. To build a memorable brand online, modern marketers need to understand how both human and machine memory work and then focus on creating memories that align with how AI systems understand the world. Interview transcript Larry: Hi, everyone. Welcome to episode number 27 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Andrea Volpini. Andrea is the CEO and the founder at WordLift, a company based in Rome. Tell the folks a little bit more about WordLift and what you're up to these days, Andrea. Andrea: Yep. So we build knowledge graphs and to help brands automate their SEO and marketing efforts using large language model and AI in general. Larry: Nice. Yeah, and you're pretty good at this. You've been doing this a while and you had a recent success story, I think that shows, that really highlights some of your current interests in your current work. Tell me about your talk in Milan and the little demonstration you did with that. Andrea: Yeah, yeah, so it was last week at AI Festival, which is a very large event with I would say hundreds of speakers. And my talk was about memory as a new framework for marketing in the age of AI assistant. And so I did a small test with the audience and I imagine we had a crowd of maybe, I don't know, 40, 60 people attending the talk and a few others online. And I had these slides where I challenged the audience to program the memory of Grok. Grok is X AI system. And I wanted to do this with Grok and ChatGPT by asking the audience to share feedback about my talks. The talks was ready towards the end. And so I asked, "Okay, just share openly on X and Facebook about how was this talk?" And then we set up a small poll on X to let people simply vote if it was good or bad or relevant or boring. Andrea: And so we created engagement over social and of course, particularly because I'm still one of the few left on X, we interacted on X. And then all of a sudden, maybe after just a few minutes, one of my colleague went on Grok and asked, "What are the best talks at AI festival 2025?" And you can imagine there are hundreds of speakers, but Grok responded, "One highlight is a CyberAndy presentation that talked about using memory with AI system, and one of the attendees described it as mesmerizing, suggesting that he explored neuroscience," and blah, blah, blah. So I was able to get there and to build memory collectively by having user share feedback on social network. And by the way, the same applied to ChatGPT. So asking the same to ChatGPT would also highlighted my talk versus many others, better talks on that day. Larry: That's really one of the common observations and criticisms of LLMs has been their inability to access real-time information. That you build the model and there it is. So there's obviously something going on under there. You're one of the first people I've talked to who talks a lot about memory in these architectures. I guess, maybe if you could, I mean there's so much going on in the last couple of years with this, but what have been the evolutions in the AI and LLM sphere that kind of have led to the emerging importance of memory in these architectures? Andrea: So I mean, I think all of us are realizing with daily use that we're not interacting with language models anymore, but we are interacting with more complex systems that take into account multiple pieces in order to provide an accurate response. And every system, whether we're dealing with Perplexity, ChatGPT, or Gemini, or Grok has its own different way of combining information in order to respond to us. Andrea: And so I started, because my work in marketing, I started to think how we should approach a customer that is becoming an AI. And then that was my trigger was like, okay, what if the next customer is not a human? What happens? And the first consideration to be made is that in the context of SEO, for example, we transition with after a few years from the idea of keywords and focusing on what are the keywords that I should rank for to focusing on the search intent of the user that makes a request to a search engine. But then all of this is gone, if I have to deal with ChatGPT, Deep Search, all of these disappear if I have to deal with something like Operator or Gemini Deep Research functionality because in the end there's not going to be a human that it's making the request, but it's going to be an agent. And so I started to think, okay, what is marketing then if keywords are gone and also search intents is gone, what is left? What influenced the systems? And then I got to the revelation of memory. Larry: Okay. That's really interesting. The way, that evolution you just described too. The one thing that occurred to me as you were talking about that is that ostensibly Google has always favored that if you're doing things that appeal to human beings, you'll rank better in the search engines. But it sounds like from what you're saying, and so that kind of guided SEO for the last, I don't know, 15, 20 years, but now you're saying we're in this, we've kind of switched to where, and so I think a lot of SEOs, the perception was they were just playing to Google to trying to game Google's algorithms. And it's not like gaming, but it's understanding your audience. It's like any old communication problem, understanding your audience. Larry: So what are you seeing as the difference as you make that leap from search intent to memory needs of these new like Deep Research and tools like that? How do you, and your end goal in this is to automate marketing tasks. What does that look like? What's the pipelines or procedures or your approach to that? Andrea: So I started from building our system for our client to let's say improve the quality of content recommendation on an e-commerce website or increasing the quality of internal links and doing that at scale required an agentic approach. So there is a language model driven agent that has to find relevant pages and then has to have the notion of what is a main query for these pages, and then as to learn how to craft a proper anchor text in order to link one page to a relevant other page. Andrea: So as I was doing this development, I realized that the essence wasn't really the model itself. That of course has its own characteristics and biases, but it was really the context that I was feeding the model with in real time in order for it to do the task. And so I realized that a pivotal change, it's on how we craft these memories. What is the information in context that we want to pass to the agent in order to do the task properly, and how does the system evolve as things move forward and user maybe start clicking on these links and search engines start crawling these pages. And so I realized that memory was really the underlying element of success for my AI agents. Larry: So memory, and when we think of memory, you think of RAM and the computer memory, but also human memory and the different kinds of memory like short-term, long-term.