Guest: George Swetlitz, CEO and Co-Founder at RightResponse AI
Host: Seth Earley, CEO at Earley Information Science
Published on: October 27, 2025
Join us for a compelling episode of the Earley AI Podcast as host Seth Earley sits down with George Swetlitz, CEO and Co-Founder of RightResponse AI. George brings decades of expertise in natural language technologies, enterprise AI adoption, and building advanced models to solve real business challenges—especially in the realm of customer engagement, feedback, and competitive analysis.
Tune in as George shares how AI-powered systems are changing the way organizations capture, understand, and act on customer feedback to deliver more relevant, personalized, and valuable experiences. He discusses why sounding “human” isn’t enough, the importance of contextual relevance, and how to transform the review response process at scale for both efficiency and revenue growth.
Key Takeaways:
Relevance Over Sounding Human: The real power of AI in customer experience lies in delivering contextually relevant responses, not just in mimicking human conversation.
Granular Sentiment Analysis: Advanced AI systems can break down reviews into meaningful phrases, better identify true intent and sentiment (even with sarcasm), and map feedback to business KPIs.
Building Fact Repositories: Onboarding AI involves creating a dynamic library of facts drawn from reviews, responses, and website content, enabling responses that are tailored to specific, high-value customer concerns.
Operational Impact at Scale: Large organizations can redeploy significant resources by automating repetitive review responses, freeing up staff to focus on complex, high-touch customer problems.
Personalized Review Requests: AI can personalize review requests by incorporating context from customer interactions, dramatically improving conversion rates and generating more insightful customer feedback.
Competitive Insights: AI-driven analysis of both your reviews and your competitors’ can highlight where you’re outperforming or falling short—especially at the hyperlocal level.
Future of AI in CX: As AI models become more advanced, onboarding and implementation will become smoother, and the quality of customer engagement will only improve.
Insightful Quote:
“What you’re trying to do with AI is get the best of both worlds. You’re trying to be relevant to somebody in the space or in the place that they’re in… The best customer service rep would do that. And now, at scale, AI can help organizations truly meet customers where they are.” - George Swetlitz
"When we use AI to look at sentiments, we pull in the review, we break it down into phrases. AI does a really good job of understanding context and sarcasm that typical natural language processing doesn't handle well." - George Swetlitz
Listen now and discover how leveraging AI in customer feedback can transform both experience and outcomes!
Links
LinkedIn: https://www.linkedin.com/in/george-swetlitz-7b43812/
Website: https://www.rightresponseai.com
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Podcast Transcript: AI-Powered Customer Feedback and Competitive Intelligence
Transcript introduction
This transcript captures a conversation between Seth Earley and George Swetlitz on how AI is transforming customer feedback analysis, review response automation, and competitive intelligence. Topics include the difference between sounding human and being contextually relevant, building fact repositories for personalized responses, and leveraging AI to improve both efficiency and revenue in large enterprises.
Transcript
Seth Earley:
Welcome to the Earley AI Podcast. My name is Seth Earley, I'm your host, and we are going to be exploring how artificial intelligence is reshaping the way organizations manage information, create value, deliver better customer experiences. Today, we're going to be talking about the intersection of customer engagement and customer feedback with AI, and we're going to be talking about how the emerging technologies can be applied to delivering more meaningful interactions and being able to get better feedback from the marketplace about our customers, their experiences, and the competition. What is the competition doing?
So joining me today is George Swetlitz. He's CEO and co-founder of RightResponse AI. George has a lot of expertise in natural language technologies, enterprise AI adoption, and applying advanced models to solve real business problems. His work focuses on building systems that go beyond generating text to really understanding how customers are interacting, what kinds of feedback they're providing, and again, what that competitive landscape is looking like. So, George, welcome to the show.
George Swetlitz:
It's nice to be here, Seth. Thank you.
Seth Earley:
So, let's talk a little bit about—I want to get to exactly what you do and what your company does in a minute, but from your exposure in the marketplace and your talking to organizations, what are the types of things that they're not quite understanding? What are some of the big misconceptions that you're finding in the marketplace about AI and about how emerging technologies are impacting the customer experience and that whole feedback mechanism of getting that customer sentiment, understanding how they're interacting, and what their experiences really are. What are people missing, or what are they not getting?
George Swetlitz:
Yeah, I think what people are—you know, people know what they know, and they don't know what they don't know. And so, as they get exposed to things, and as they get exposed to AI, they learn about what works and what doesn't work. And what's happened in this space around customer experience is that people—leaders in organizations don't really understand what's the best way to leverage AI in the customer experience, customer sentiment space. And they're focused on the kinds of things that they see AI do, which is sounding human, as opposed to what I counsel people, which is about being contextually relevant, right? Using AI to be relevant to your customers, as opposed to sounding human.
Seth Earley:
That's an interesting distinction, and obviously sounding human is important, but it has to be relevant, right? Doesn't matter if you're sounding human, and you're not addressing my problem, or you're not giving me any insights, but sounding human is fine, but give me something that's relevant and meaningful. So, tell me about how you look at this marketplace, and how you look at customer experience, and give me a little bit of a sense of the kinds of things that you do in this environment, how you're leveraging AI, and how you're impacting the customer experience. A little bit of a company commercial, but not too salesy.
George Swetlitz:
Right. Now, just to build on what you just said, though, you know, it's the flip side of sounding human. It's, you know, when you call a customer service agent and they read you back the script. Well, they're human, but what you say is, well, they're robotic, they're robots, they can't help me. And so, what you're trying to do with AI is get the best of both worlds. You're trying to be relevant to somebody in the space, or in the place that they're in.
Seth Earley:
Right.
George Swetlitz:
And that's how I view the world. And so, you know, what we do with AI is to do the things that large organizations need to be relevant to their customers. So what are large organizations looking for? They're looking for efficiency. You know, most large organizations have centralized services, and people struggle with centralization versus decentralization because of the loss of quality when you bring things to the center, right? And they also want revenue.
Seth Earley:
They also want what? I'm sorry, go ahead.
George Swetlitz:
Revenue. They want revenue, right? They want to build revenue.
Seth Earley:
Important stuff, money's nice.
George Swetlitz:
Very important. And so we try to help do those three things by leveraging the review ecosystem. Right? And so, you know, I can talk more about how we do that, but that's essentially what we're doing for our larger organizations.
Seth Earley:
And so, what is different? Like, because people have been doing sentiment analysis for a very long time, they've used text analytics, they've used different mechanisms to kind of harvest, you know, data, social media data, and listen to customers in one way, shape, or form, and, you know, lots of different ways of getting that voice of the customer. So, how is it different? What is different now?
George Swetlitz:
Right. Okay, so focusing in on sentiment analysis. AI in general has upped the game with sentiment analysis by allowing the phrasing to be looked at in context. So, you know, when you have sarcasm, or when you have various things like that, AI does a much better job of understanding that. In a review, for example, if somebody said, well, I went to this one restaurant, and I was unhappy, and then I came here and was happy. Typical natural language processing doesn't do a great job of saying, well, that first part is really not about the company that I'm evaluating. AI does a really good job of that. So, when we use AI to look at sentiments, we pull in the review, we break it down into phrases.
We look at each phrase for sentiment, and we look at sentiment trends over time. And then we aggregate it. So you get much more granularity than you got with the traditional sentiment analysis. So that's the first part. The second part is that what we're trying to do is not just respond to a review, but we're trying to help our customers, our clients, build a fact repository.
And you build that fact repository from a combination of the reviews that come in, the responses that you've written, the website for the company. So when we onboard people, you know, they come in, they say, okay, here's our website, here's all this information, and we do a process that takes this raw information and turns it into facts.
And those facts are the key to making it personal and relevant. So you can just put a review into ChatGPT, and it'll give you a very nice sounding response, but it will not be actually valuable. It won't have the key information, okay? And so what you're doing is you're taking your company's key information, breaking it down to facts, and then having them be able to respond automatically when those issues come up in a review. So you're both relevant because you're responding to the particular concerns in that review, and you're responding with correct information from a company as opposed to hallucinated information.
Seth Earley:
Right, so this is where, you know, RAG—retrieval augmented generation—comes in, where you're supplementing the large language model with knowledge about a company, its services, its products, its, you know, policies, whatever. And then it's taking the information and summarizing it and presenting it back to the customer in a form that's, you know, consumable.
George Swetlitz:
That's right. And one of the things that happens in this is that you can't just dump information into a prompt. So you have to be really selective about what goes into a prompt. And by building this fact library, you can be very, very selective about what information is most critical, most important, and most relevant to the particular customer concern.
Seth Earley:
And so how are you building that? Are you doing that automatically, or is there a process with the client where you're interviewing them, getting information? What does that look like?
George Swetlitz:
So we start with their website, and we start with some of the responses that they've written in the past. And we use AI to extract potential facts from that information. And then we have a human review those facts, refine them, add to them. Because what happens is when you start responding to reviews, you find that there are questions that come up that you didn't anticipate.
And so you want to have a process to continuously add facts. So it's a combination of automated extraction and human curation. And over time, as you get more and more reviews and more and more responses, that fact library becomes richer and richer. And what you find is that the more facts you have, the better your responses are, the more relevant they are, the more helpful they are to customers.
Seth Earley:
So I'm imagining in a large organization, you might have different business units, different product lines, different geographies. How do you manage that complexity?
George Swetlitz:
Yeah, so what we do is we allow our clients to organize their facts by different categories. So you might have facts that are about a specific product, facts that are about a specific location, facts that are about corporate policies. And then when a review comes in, we use AI to determine which facts are relevant to that particular review. So if someone's complaining about a specific location, we'll pull in the facts that are relevant to that location. If they're complaining about a specific product, we'll pull in the facts about that product.
And this is where the orchestration of multiple AI agents comes in. We're not just using one model to do everything. We have different agents that are specialized for different tasks. One agent might be identifying the key themes in a review. Another agent might be selecting the relevant facts. Another agent might be crafting the response. And they all work together to create something that's relevant and accurate.
Seth Earley:
That makes a lot of sense. And I imagine one of the benefits is that you can have consistency across all your responses, but still have personalization.
George Swetlitz:
Exactly. That's the key. You want consistency in your brand voice, consistency in your messaging, but you also want each customer to feel like you're speaking directly to them about their specific concern. And that's what AI enables. It enables you to scale personalized responses in a way that would be impossible if you were doing it manually.
Seth Earley:
So let's talk about some use cases. What are some examples of companies that are using this successfully?
George Swetlitz:
Sure. So we work with a number of large enterprises. One example is a large ferry company. They operate multiple routes, multiple vessels, and they get reviews on all the major platforms—Google, Yelp, Facebook, TripAdvisor. And what they found before they started working with us was that they just couldn't keep up with responding to reviews. They had maybe one person trying to respond, and they could only respond to a small fraction of the reviews they were getting.
And the quality was inconsistent because that person was stretched thin. What we've been able to do is help them respond to virtually every review that comes in, with responses that are relevant to the specific concerns in each review. So if someone's complaining about parking, we have facts about where parking is available and what the options are. If someone's complaining about the onboard experience, we have facts about the amenities and what they can expect.
And what they've found is that their star rating has gone up because people see that the company is engaged, that they're responsive, that they care about feedback. And they're also able to identify trends. So if they start seeing a lot of complaints about a specific issue, they can address it operationally before it becomes a bigger problem.
Seth Earley:
So you're providing not just response automation, but also intelligence about what's happening in the business.
George Swetlitz:
Exactly. And that's where the revenue piece comes in. Because when you understand what customers are saying, when you understand what they care about, you can make better business decisions. You can prioritize improvements that will have the biggest impact on customer satisfaction. You can identify new opportunities. You can see what your competitors are doing well or poorly.
Seth Earley:
So you mentioned competitors. How does that work? Are you analyzing competitor reviews as well?
George Swetlitz:
Yes. So one of the things we do is we can pull in reviews for a company's competitors and analyze them the same way we analyze the company's own reviews. And what this allows you to do is see where you're outperforming competitors and where they're outperforming you. And you can do this at a very granular level. So you might find that at a specific location, a competitor is getting better reviews about cleanliness, or speed of service, or whatever the relevant factors are.
And this gives you very actionable intelligence. You can say, okay, at this location we need to focus on cleanliness. At this other location, maybe we're doing fine on cleanliness but we need to work on speed. And you can benchmark yourself against competitors in a way that's very specific and very actionable.
Seth Earley:
That's really valuable. So you're essentially turning reviews into a competitive intelligence tool.
George Swetlitz:
That's right. And what's interesting is that reviews are public information. Anyone can look at them. But most companies don't have a systematic way of analyzing them at scale. They might spot check a few reviews here and there, but they're not doing a comprehensive analysis across all their locations, all their products, all their competitors. And that's what AI enables.
Seth Earley:
So let's talk about the review solicitation side. Because you mentioned that's also part of what you do.
George Swetlitz:
Yes. So the traditional way of soliciting reviews is you send someone an email that says, hey, please leave us a review, here's a link. And those have pretty low conversion rates. What we've been able to do is personalize those requests. So if we know that someone had a specific interaction with your company, we can reference that in the review request.
For example, if someone bought a specific product, we can say, hey, we hope you're enjoying your new widget, we'd love to hear about your experience. And that personalization dramatically increases conversion rates. We've seen conversion rates go up by 2x, 3x, sometimes more, just by personalizing the review request.
Seth Earley:
And presumably the reviews you get are also more detailed and more useful.
George Swetlitz:
Exactly. Because when you ask someone a specific question, they're more likely to give you a specific answer. If you just say, hey, leave us a review, they might write two words. But if you say, hey, we'd love to hear about your experience with product X, they're much more likely to give you detailed feedback.
Seth Earley:
So you're improving the quality and the quantity of the reviews.
George Swetlitz:
That's right. And then you have this virtuous cycle where you're getting more reviews, you're responding to them with relevant, helpful information, your ratings go up, more people see your business, more people choose your business. It's a real competitive advantage.
Seth Earley:
So let's talk about implementation. What does it take for a company to get started with this?
George Swetlitz:
So the first step is we work with them to build their fact repository. As I mentioned, we start with their website, their existing responses, any other documentation they have. We extract potential facts, we work with them to refine those facts, add to them. That process usually takes a few weeks.
Then we integrate with their review platforms. Most of our clients are on Google, but we also integrate with Yelp, Facebook, TripAdvisor, industry-specific review platforms. And we start responding to reviews. At first, we usually have the client review the responses before they go out, just to make sure they're comfortable with the tone, the content, the accuracy. But pretty quickly, most clients move to a model where the responses go out automatically and they just spot check them.
Seth Earley:
So there's a trust-building process.
George Swetlitz:
Absolutely. And that's important. We want clients to feel confident that the system is representing their brand well. And the way we build that confidence is by starting with a review process, and then gradually moving to more automation as they see that the quality is consistently high.
Seth Earley:
So you mentioned earlier that you work with large enterprises. Can smaller companies use this too?
George Swetlitz:
Yes, absolutely. We have clients of all sizes. For smaller companies, the benefit is that they can have a level of sophistication in their review management that they couldn't afford to do manually. You know, if you're a small business owner, you're wearing a lot of hats. You might not have time to respond to every review. But with our system, you can.
For larger enterprises, the benefit is scale and consistency. If you have 100 locations, or 1,000 locations, you can't have someone at each location writing custom responses to every review. But with AI, you can have that level of personalization at scale.
Seth Earley:
So what about other industries? You mentioned a ferry company. What other types of businesses are good fits for this?
George Swetlitz:
We work with a wide range of industries. Hospitality is a big one—hotels, restaurants, attractions. Retail, both brick-and-mortar and e-commerce. Healthcare, though that has some unique considerations around privacy. Professional services. Really any business that gets reviews and wants to manage them effectively.
One interesting example is we work with a large software company. They get reviews on Google, Trustpilot, the App Store. It's a much more vertical as opposed to horizontal business. Thousands of reviews a month. But it's essentially the same problem. You know, they have issues with the app, they have issues with the website, they have issues with these different things, and how you break it down, understand it, get the right information back to the right people at the right time.
And take care of your customers, right? What they've found, but using—so they have, like, 25 facts. They spend a lot of time developing their facts, great facts, very rich. So if somebody complains about something about the app, they have this very sophisticated response, very informative response that gets incorporated. Customers love it, right? Really helps change the impression of what's going on.
Seth Earley:
Sure.
George Swetlitz:
Because the problem is, is that if somebody says something wrong in a review and you don't rebut it, all the review readers think that's the truth.
Seth Earley:
Right.
George Swetlitz:
You know, you have to engage in that ecosystem. And unfortunately, you know, most companies just don't. They just don't engage.
Seth Earley:
Yeah, yeah, they don't...
George Swetlitz:
Primarily because they don't know they can.
Seth Earley:
Okay, yeah. So, so, are there bandwidth issues, and logistic issues, and tactical issues, and so on? Or you might say it's a dot on their radar, right? It's not that important. But, you know, and I imagine that all this intelligence, this market intelligence and voice of the customer, obviously always goes back upstream to try to fix whatever problem you have, right? So, is it a lack of onboarding? Is it a lack of the right help? Does a screen need to change? Does a customer service response need to—whatever it might be, but you're going back upstream to the source of the problem and doing that remediation so that you don't get those kinds of responses in the future. So that's highly valuable.
And then I do like the idea of understanding the gaps in the competitive landscape to say, you know, where are we missing the information. I was going to say missing the boat, but I didn't want to do that, because you're a ferry example. Where are we missing information? Where are we failing the customer? Where are our competitors, you know, outperforming us in these different areas? So very, very valuable.
And I can see, you know, how it's a kind of an evolution of typical sentiment analysis, because you're going a step further, and you're trying to understand more of these factors in a broader way. Let's just talk about where do you see the future of this going, and what are your thoughts about, you know, what organizations need to do to kind of get the best use from, you know, AI in their customer experience. So where is it going, and what do companies need to think about?
George Swetlitz:
Yeah, I think AI's just gonna get better. This stuff's not going away. I think AI, you know, what I've seen, so what my experience with AI has been that from where we were at the very beginning when we started this, we have many more agents now than we had before, because we find that quality is higher the smaller the ask.
Right? If you ask AI to do 50 things—I'll just give you a simple example. If there are 25 facts, and we would feed 25 facts in a prompt and say, which of these are relevant, it would always get the first one right, and it would always get the last one wrong, right? It just gets lazy and tired. But if you ask 25 different times, you get the right answer each time. And it's a tiny bit more expensive, but not that much. And so, you know, AI will get better at handling these kinds of things over time, and it'll make the system more robust.
Right? That's kind of what I see AI changing. I think the other thing that will make this easier is when we start onboarding people with AI, because getting these things set up, you know, we have a team, right, that onboards people, because it's one thing just to go in and give a generic AI response, anybody can do that, right? It's easy. But when you start making it more—when you make the system more robust, it's harder to set up, right? And so our larger enterprise customers, they'll have somebody, and we have somebody, and we help them, but I think longer term, as AI gets even better, we're going to be able to do kind of AI onboarding, and that will help people migrate to these systems much, much more easily.
Seth Earley:
Hmm, okay, great. Well, listen, George, thank you so much for your time today. I appreciate your thoughts. We'll have your information in the show notes, but where can people find you?
George Swetlitz:
So, RightResponse AI is our website. We have a special place, RightResponse AI slash podcasts slash EarleyAI, where they can go, and we have a special coupon for your listeners. If they have any interest, they can book a call with me, directly with me, and they can get a coupon code to get free credits to help them learn more about the system.
Seth Earley:
Okay? You're gonna measure our effectiveness. Great, okay.
George Swetlitz:
Exactly.
Seth Earley:
I'm a big believer in quantitative measures, so that's great. Again, thank you so much for your time, I appreciate it, and thanks to our listeners. We will see you next time at the next Earley AI podcast. Thank you for joining us, and we'll see you next time.
George Swetlitz:
Thank you, Seth.