Dear founder,
This week, I want to be as pragmatic as possible. Let's talk about not just "AI this and AI that", but the actual applications of generative AI that I leverage within Podscan to get my customers to see the value of the product as quickly as possible.
You know, it took me a long while to realize this: AI isn't just something like a chatbot for my customers. AI can work behind the scenes to facilitate getting the right stuff in front of the right people for me, even just to figure out who people are and how I should talk to them. And today, I want to share exactly what I'm doing, how expensive this is to run, and how I believe this can be part of every single software as a service business out there—even if you don't have any touchpoint with artificial intelligence in that business at all. Even if you don't think you should be offering AI features to your customers.
Speaking of scaling smarter, not just faster—that's exactly what today's sponsor, Paddle, is all about. They're running an exclusive five-part online live series designed for SaaS and digital product leaders who want to scale with precision. From pricing strategies to exit planning, each session is packed with expert insights, real-world data, and proven strategies. If you're serious about growing your SaaS business the right way, check out Paddle.com's event series. Because just like the AI strategies I'm about to share with you, it's all about working smarter to create those magical moments for your customers.
The Six-Hour Score That Changed Everything
Let me start with the internal use of generative AI that has been absolutely transformative for Podscan. Six hours after a customer signs up for their trial—exactly six hours—something magical happens. But they don't see it. It happens completely behind the scenes.
I let them explore the platform first. But at that six-hour mark, I score them. Or rather, I have an LLM that is fed with a lot of information score that customer for me on a scale from zero to 10. I take everything that I know about this person up until that point and have the AI come up with a number following a rather complicated prompt on how to score them.
Here's what I collect: the domain of their email address, their name, the name of the team they created. I track what and how often people search and the general themes of the topics they search for. I collect all the activities they have on the page—if they navigate to the dashboard, if they navigate to a search window, if they navigate to the API documentation, or just which podcasts they checked out along the way. I take all of these activities that I track very meticulously in a database that at this point has over 500,000 activities in it.
I condense all of this into a prompt—I think at this point it's GPT-4o still—to come up with a JSON object, a data object that contains a score and an explanation for why that score was given. I set up the prompt so that it gives me the explanation first and then gives me the score. Usually, that's generally a good idea for these LLM systems that generate one token after the other—to always give them reasoning ability first and then any summary, any scoring, any reduction of granularity of information should come after they actually argued the case.
And if they have a score of five and above, they should then answer not just with the score, but with a full data object containing information about this customer, this potential customer, and how I should interact with them in the future.
The prompt is incredibly specific. I instruct it: "We're trying to talk mostly to these kinds of customers—PR agencies, marketing customers, and founders. So score them higher if they're a founder. And if they are a founder, figure out what projects they're currently working on, so we can talk to them about these projects. Suggest particular things on how to use the platform with their specific problems and targets in mind."
If it's a business domain—not just Gmail—I instruct the prompt to look up that website and figure out what kind of business they are. I very intensely instruct the AI to actually check who is signing up, if they know this email address, if they know the name, and can give me more social feeds to reach out to them or to look for their work.
I send this message to my Slack if it's a high-scoring prospect, so I can start reaching out manually to them. And I persist this score, the reason for the score, and the additional data to the user object, so I can use it later.
The Magic of the Generate Email Button
In my administrative interface, I have a view that lists every single prospect over the last 10 days—which is the length of a trial—showing their score, what activities they've taken up until now, their email, all of that. And then at any point when I feel like reaching out to them, I have a generate email button.
That generate email button takes all of the activities into account. It takes their score, their previous extraction, who they are, what we know about them, what they've said about themselves in the profile, what teams they are on, what alerts they've already created, what searches they have done. It takes all that into account and creates a follow-up email that looks at the things they have done and tries to find the next best thing that they should be doing on Podscan to see the value of the platform.
I've instructed that particular email-generating prompt to come up with the best possible keywords for an alert, or the best search terms for a search if they haven't searched before, and suggest that one thing to them in an email. The email introduces me as the founder. It says, "Hey, this is the next step you could take. Any questions? Just respond to this email." And then I send that manually by taking that content and putting it into my email window.
This has been extremely helpful in getting people who have done little on the platform to actually set up their first alert, and getting people who've already seen a lot of value by searching to persist that search into an alert. Because that's my main goal with Podscan—to get people to set up an alert. Because an alert generates value memory, like value-nurturing notifications. If somebody puts their brand in there and they get one or two mentions on a podcast every single day, that's great. They get an email from me every single day with something interesting. That is valuable, value-nurturing that just happens through the platform itself.
This has been running for a couple months at this point. It's extremely helpful. It has led to a lot of interesting conversions and even conversations with people who were just checking it out, not doing much. But a day or two later, I reached out to them with a suggestion. They set up the alert, and now all of a sudden, they are extremely happy with the product, and they're paying for it.
The Onboarding Wizard That Reads Minds
There's a second step, one that comes much earlier, one that I only recently implemented because I realized I've been missing this all along. And that is during the first onboarding wizard that new signups have on Podscan.
I had onboarding for over a year—whenever you come to the dashboard for the first time, a fullscreen overlay comes up that says, "Hey, what do you want to do? Do you want to look for mentions? Do you want to search? Or do you want to just check out the product?" Click any of these, and it takes you to that section.
If you clicked on monitoring—and that was the default up until a couple of days ago—you would then be able to write a couple of filters or keywords that you might find interesting, put them into a little list and click "Create my first alert." But it still involved a lot of manual work from people who just came to this product. They want to see what it can do.
So instead of having people write their own keywords, I implemented an automated background process that would fetch or generate the right keywords for this person while they are going through the onboarding process.
During registration, I have a step where I ask people to self-classify. They can classify as a founder, as somebody who owns a podcast, as somebody who analyzes data for a living in PR or marketing, or alternatively, "I just want to check it out." Between these four groups, the ones I'm most interested in are obviously the data analyst and the founder, but the others might also just be on their first step into becoming a customer.
I also have a text field during registration where they can say what their project is, why they come to Podscan. Some people tell me, "Oh, I'm looking for this particular transcript," or "I work for a PR agency, and we're trying to track what people say about our clients." People put that in there, maybe in 20% of cases. But the people who do help a lot with the system.
I take this information, I take the domain from the email they just signed up with and their full name, and I throw this into a very fast model—I think this is GPT-4o mini. And I task it to create three good keywords or keyword groups that might be good alert keywords that will be potentially useful to this person.
The selection is incredibly effective. If you sign up from a Google domain and you say you're just looking for a transcript, it suggests an alert that has to do with the podcast you might be interested in. If you come from an educational domain, you work at a university, and you say you're a data analyst, then it suggests some keywords, maybe even some key personnel names from your university to track. If you come from a marketing company that the AI is aware of, an agency that has certain clients, the AI will check their website, figure out who their biggest clients are, and suggest tracking their clients.
That is the magic in this moment. That is what the LLM can do with tool calling and with scraping websites—it can actually fetch meaningful information that helps onboard the customer to their specific use case. All from just a self-classifier, maybe a project description, and their email domain.
The Dashboard AI Builders
The third way I use generative AI is in the dashboard itself. Some people just don't like onboarding. They say, "No, I'm going to do this myself." But I still wanted that magic of having the system create an alert for you to be part of the dashboard experience, to be a repeatable thing that people can do. It's not just their first alert that they get to create via AI—it's also every other one they do in the future.
So I implemented an alert builder. You can just free-form write about whatever you want. You can say, "Hey, I want an alert that tracks keywords from this industry. I'm using it to create a newsletter," and whatever. And then an AI tasked with a lot of information about the platform creates the best possible keywords and other specific information, like the context-aware question filter that I offer.
This is an AI-assisted check that runs on every single transcript that gets certain keywords mentioned, just to make sure the context is exactly what the user wants. Because if John Smith is looking for a mention, then a lot of John Smiths that that guy might not be interested in might be mentioned too. So having a specific keyword filter—things like "Is this episode talking about John Smith, the professional basketball player?"—will get a lot of false positives out of the way. And that can also be pre-suggested by AI.
I've trained this AI through prompting to give really good, solid results that work really well for Podscan data. The alert builders are available in the dashboard for everybody. The list builder, which creates lists of podcasts, has several features where you can just say, "I want all podcasts that talk about Star Trek: The Next Generation, like rewatch podcasts or humor comedy podcasts that have a sci-fi angle," whatever it might be. You say what you want, and then the list gets generated.
It pulls all of these podcasts from our own API. It searches for keywords. It pulls those in. It looks for similar podcasts, because we have podcast similarity—I built that over the last year as well. And it just explores the whole system, scores them, and returns the list of the best 50 or 100 items, which is all also powered by an AI system that gets the best search terms and finds the best scoring criteria for that process.
The Philosophy: AI as a Translation Layer
For me, generative AI means not having a chatbot in there. I don't need that. It doesn't mean any kind of overly agentic behavior. Using generative AI in Podscan, in the way I communicate with the user, is about making it magical for them to be understood. For them to see that Podscan gets why they are using it. It understands that for their business, they might be looking for this, so I'm going to suggest that. So they see, "Oh, this is what I should look for."
And in a moment like this, where they already get a good suggestion, they also understand how they should be searching for things, how they should be phrasing things to make Podscan be used more optimally and most effectively.
To be able to do this, I did a painful walkthrough of the system where I recorded myself just explaining every single feature on the platform into a transcription software. Then had that transcription thrown into Claude, and had that boiled down to a markdown document describing the structure and function of the platform. That markdown document is a couple thousand lines long. That was then condensed into the prompts that generate all these helpful steps.
The prompts are not small. They're quite huge, because they have a lot of context about what Podscan is and how Podscan can best be used. Particularly the prompt that tells people what their next best step is—the one that I manually trigger via email—that needs to know what all the potential steps are. So I had to go through the whole platform, had to describe it from start to finish, and that becomes part of the prompt.
The Economics of Magic
Looking into this, to whatever kind of software business you might be running, I think this is a very, very useful and quite cheap thing to do. Because it only ever runs once, right? My scoring happens per customer—it's only one request that comes back with a score and a little bit of text. It doesn't really have too much context other than the name of the customer, the email, maybe a couple things they searched for, maybe a couple locations they've been in the product. It's not big. This costs me less than a couple cents, maybe not even a cent in fees.
Same goes for the email generated to the customer. That also may be a cent or two just in cost to pull in all the information and the prompt cycle. So that might be more expensive, but it's still like per customer, let's say five cents. I think that's very manageable per prospect, and I do it only for certain prospects that score fairly high. So on a daily basis, I spend maybe 20 cents on this. It's negligible.
At the same time, AI in the product can be quite expensive if people use it a lot, so I'm very, very cautious with anything that allows a customer to trigger an AI API request. It's heavily rate-limited within Podscan's infrastructure. If I see somebody using AI requests of any kind—they all go through the same middleware—more than a couple times an hour, then I can manually decide if I should block this for this customer or deactivate the account if they're starting to abuse it.
You have to be careful with that. With backend processes, it's always good to make sure that you don't overuse AI API calls just for your own sanity. I would track them all and see if you go over a certain threshold per hour, you should get an alarm.
The Universal Application
I think tracking who your users are, understanding what their needs are, and suggesting initial configuration for their dashboards or for their use of your software—that can work in every single niche. That can work in every single industry. I see a lot of cutting-edge founders building this into every single product they create, because it is always valuable to meet the customer immediately where they're at.
And this is easiest to do if you investigate where they're coming from. What information do I have about this person? How can I make it easiest for them to see where the value is?
Inside your product, you can also make AI a kind of transmission system between what the customer knows they want and how you need it presented to your database or your algorithms or your backend system. AI can communicate between these two because you can put in the effort to completely and correctly describe your system, and then task AI to translate between user requirements and your platform requirements.
That is another magical moment. It's the AI doing work for the user. It's almost an agentic thing. You have this little transmission process that an AI does for you as a user, and all you see is the data you want to get, and you just describe it with natural language.
This is the one way that I think every business can benefit from AI, and I highly recommend you implement it. This might even be a business idea, for that matter. So, you know, take a look at it.
The goal isn't to wow people with AI. The goal is to use AI to help people reach their own wow moments with your product. And when you do that invisibly, behind the scenes, creating those magical moments where everything just works exactly as they hoped—that's when AI becomes truly powerful for bootstrapped founders.This week, I want to be as pragmatic as possible. Let's talk about not just "AI this and AI that", but the actual applications of generative AI that I leverage within Podscan to get my customers to see the value of the product as quickly as possible.
You know, it took me a long while to realize this: AI isn't just something like a chatbot for my customers. AI can work behind the scenes to facilitate getting the right stuff in front of the right people for me, even just to figure out who people are and how I should talk to them. And today, I want to share exactly what I'm doing, how expensive this is to run, and how I believe this can be part of every single software as a service business out there—even if you don't have any touchpoint with artificial intelligence in that business at all. Even if you don't think you should be offering AI features to your customers.
Speaking of scaling smarter, not just faster—that's exactly what today's sponsor, Paddle, is all about. They're running an exclusive five-part online live series designed for SaaS and digital product leaders who want to scale with precision. From pricing strategies to exit planning, each session is packed with expert insights, real-world data, and proven strategies. If you're serious about growing your SaaS business the right way, check out Paddle.com's event series. Because just like the AI strategies I'm about to share with you, it's all about working smarter to create those magical moments for your customers.
The Six-Hour Score That Changed Everything
Let me start with the internal use of generative AI that has been absolutely transformative for Podscan. Six hours after a customer signs up for their trial—exactly six hours—something magical happens. But they don't see it. It happens completely behind the scenes.
I let them explore the platform first. But at that six-hour mark, I score them. Or rather, I have an LLM that is fed with a lot of information score that customer for me on a scale from zero to 10. I take everything that I know about this person up until that point and have the AI come up with a number following a rather complicated prompt on how to score them.
Here's what I collect: the domain of their email address, their name, the name of the team they created. I track what and how often people search and the general themes of the topics they search for. I collect all the activities they have on the page—if they navigate to the dashboard, if they navigate to a search window, if they navigate to the API documentation, or just which podcasts they checked out along the way. I take all of these activities that I track very meticulously in a database that at this point has over 500,000 activities in it.
I condense all of this into a prompt—I think at this point it's GPT-4o still—to come up with a JSON object, a data object that contains a score and an explanation for why that score was given. I set up the prompt so that it gives me the explanation first and then gives me the score. Usually, that's generally a good idea for these LLM systems that generate one token after the other—to always give them reasoning ability first and then any summary, any scoring, any reduction of granularity of information should come after they actually argued the case.
And if they have a score of five and above, they should then answer not just with the score, but with a full data object containing information about this customer, this potential customer, and how I should interact with them in the future.
The prompt is incredibly specific. I instruct it: "We're trying to talk mostly to these kinds of customers—PR agencies, marketing customers, and founders. So score them higher if they're a founder. And if they are a founder, figure out what projects they're currently working on, so we can talk to them about these projects. Suggest particular things on how to use the platform with their specific problems and targets in mind."
If it's a business domain—not just Gmail—I instruct the prompt to look up that website and figure out what kind of business they are. I very intensely instruct the AI to actually check who is signing up, if they know this email address, if they know the name, and can give me more social feeds to reach out to them or to look for their work.
I send this message to my Slack if it's a high-scoring prospect, so I can start reaching out manually to them. And I persist this score, the reason for the score, and the additional data to the user object, so I can use it later.
The Magic of the Generate Email Button
In my administrative interface, I have a view that lists every single prospect over the last 10 days—which is the length of a trial—showing their score, what activities they've taken up until now, their email, all of that. And then at any point when I feel like reaching out to them, I have a generate email button.
That generate email button takes all of the activities into account. It takes their score, their previous extraction, who they are, what we know about them, what they've said about themselves in the profile, what teams they are on, what alerts they've already created, what searches they have done. It takes all that into account and creates a follow-up email that looks at the things they have done and tries to find the next best thing that they should be doing on Podscan to see the value of the platform.
I've instructed that particular email-generating prompt to come up with the best possible keywords for an alert, or the best search terms for a search if they haven't searched before, and suggest that one thing to them in an email. The email introduces me as the founder. It says, "Hey, this is the next step you could take. Any questions? Just respond to this email." And then I send that manually by taking that content and putting it into my email window.
This has been extremely helpful in getting people who have done little on the platform to actually set up their first alert, and getting people who've already seen a lot of value by searching to persist that search into an alert. Because that's my main goal with Podscan—to get people to set up an alert. Because an alert generates value memory, like value-nurturing notifications. If somebody puts their brand in there and they get one or two mentions on a podcast every single day, that's great. They get an email from me every single day with something interesting. That is valuable, value-nurturing that just happens through the platform itself.
This has been running for a couple months at this point. It's extremely helpful. It has led to a lot of interesting conversions and even conversations with people who were just checking it out, not doing much. But a day or two later, I reached out to them with a suggestion. They set up the alert, and now all of a sudden, they are extremely happy with the product, and they're paying for it.
The Onboarding Wizard That Reads Minds
There's a second step, one that comes much earlier, one that I only recently implemented because I realized I've been missing this all along. And that is during the first onboarding wizard that new signups have on Podscan.
I had onboarding for over a year—whenever you come to the dashboard for the first time, a fullscreen overlay comes up that says, "Hey, what do you want to do? Do you want to look for mentions? Do you want to search? Or do you want to just check out the product?" Click any of these, and it takes you to that section.
If you clicked on monitoring—and that was the default up until a couple of days ago—you would then be able to write a couple of filters or keywords that you might find interesting, put them into a little list and click "Create my first alert." But it still involved a lot of manual work from people who just came to this product. They want to see what it can do.
So instead of having people write their own keywords, I implemented an automated background process that would fetch or generate the right keywords for this person while they are going through the onboarding process.
During registration, I have a step where I ask people to self-classify. They can classify as a founder, as somebody who owns a podcast, as somebody who analyzes data for a living in PR or marketing, or alternatively, "I just want to check it out." Between these four groups, the ones I'm most interested in are obviously the data analyst and the founder, but the others might also just be on their first step into becoming a customer.
I also have a text field during registration where they can say what their project is, why they come to Podscan. Some people tell me, "Oh, I'm looking for this particular transcript," or "I work for a PR agency, and we're trying to track what people say about our clients." People put that in there, maybe in 20% of cases. But the people who do help a lot with the system.
I take this information, I take the domain from the email they just signed up with and their full name, and I throw this into a very fast model—I think this is GPT-4o mini. And I task it to create three good keywords or keyword groups that might be good alert keywords that will be potentially useful to this person.
The selection is incredibly effective. If you sign up from a Google domain and you say you're just looking for a transcript, it suggests an alert that has to do with the podcast you might be interested in. If you come from an educational domain, you work at a university, and you say you're a data analyst, then it suggests some keywords, maybe even some key personnel names from your university to track. If you come from a marketing company that the AI is aware of, an agency that has certain clients, the AI will check their website, figure out who their biggest clients are, and suggest tracking their clients.
That is the magic in this moment. That is what the LLM can do with tool calling and with scraping websites—it can actually fetch meaningful information that helps onboard the customer to their specific use case. All from just a self-classifier, maybe a project description, and their email domain.
The Dashboard AI Builders
The third way I use generative AI is in the dashboard itself. Some people just don't like onboarding. They say, "No, I'm going to do this myself." But I still wanted that magic of having the system create an alert for you to be part of the dashboard experience, to be a repeatable thing that people can do. It's not just their first alert that they get to create via AI—it's also every other one they do in the future.
So I implemented an alert builder. You can just free-form write about whatever you want. You can say, "Hey, I want an alert that tracks keywords from this industry. I'm using it to create a newsletter," and whatever. And then an AI tasked with a lot of information about the platform creates the best possible keywords and other specific information, like the context-aware question filter that I offer.
This is an AI-assisted check that runs on every single transcript that gets certain keywords mentioned, just to make sure the context is exactly what the user wants. Because if John Smith is looking for a mention, then a lot of John Smiths that that guy might not be interested in might be mentioned too. So having a specific keyword filter—things like "Is this episode talking about John Smith, the professional basketball player?"—will get a lot of false positives out of the way. And that can also be pre-suggested by AI.
I've trained this AI through prompting to give really good, solid results that work really well for Podscan data. The alert builders are available in the dashboard for everybody. The list builder, which creates lists of podcasts, has several features where you can just say, "I want all podcasts that talk about Star Trek: The Next Generation, like rewatch podcasts or humor comedy podcasts that have a sci-fi angle," whatever it might be. You say what you want, and then the list gets generated.
It pulls all of these podcasts from our own API. It searches for keywords. It pulls those in. It looks for similar podcasts, because we have podcast similarity—I built that over the last year as well. And it just explores the whole system, scores them, and returns the list of the best 50 or 100 items, which is all also powered by an AI system that gets the best search terms and finds the best scoring criteria for that process.
The Philosophy: AI as a Translation Layer
For me, generative AI means not having a chatbot in there. I don't need that. It doesn't mean any kind of overly agentic behavior. Using generative AI in Podscan, in the way I communicate with the user, is about making it magical for them to be understood. For them to see that Podscan gets why they are using it. It understands that for their business, they might be looking for this, so I'm going to suggest that. So they see, "Oh, this is what I should look for."
And in a moment like this, where they already get a good suggestion, they also understand how they should be searching for things, how they should be phrasing things to make Podscan be used more optimally and most effectively.
To be able to do this, I did a painful walkthrough of the system where I recorded myself just explaining every single feature on the platform into a transcription software. Then had that transcription thrown into Claude, and had that boiled down to a markdown document describing the structure and function of the platform. That markdown document is a couple thousand lines long. That was then condensed into the prompts that generate all these helpful steps.
The prompts are not small. They're quite huge, because they have a lot of context about what Podscan is and how Podscan can best be used. Particularly the prompt that tells people what their next best step is—the one that I manually trigger via email—that needs to know what all the potential steps are. So I had to go through the whole platform, had to describe it from start to finish, and that becomes part of the prompt.
The Economics of Magic
Looking into this, to whatever kind of software business you might be running, I think this is a very, very useful and quite cheap thing to do. Because it only ever runs once, right? My scoring happens per customer—it's only one request that comes back with a score and a little bit of text. It doesn't really have too much context other than the name of the customer, the email, maybe a couple things they searched for, maybe a couple locations they've been in the product. It's not big. This costs me less than a couple cents, maybe not even a cent in fees.
Same goes for the email generated to the customer. That also may be a cent or two just in cost to pull in all the information and the prompt cycle. So that might be more expensive, but it's still like per customer, let's say five cents. I think that's very manageable per prospect, and I do it only for certain prospects that score fairly high. So on a daily basis, I spend maybe 20 cents on this. It's negligible.
At the same time, AI in the product can be quite expensive if people use it a lot, so I'm very, very cautious with anything that allows a customer to trigger an AI API request. It's heavily rate-limited within Podscan's infrastructure. If I see somebody using AI requests of any kind—they all go through the same middleware—more than a couple times an hour, then I can manually decide if I should block this for this customer or deactivate the account if they're starting to abuse it.
You have to be careful with that. With backend processes, it's always good to make sure that you don't overuse AI API calls just for your own sanity. I would track them all and see if you go over a certain threshold per hour, you should get an alarm.
The Universal Application
I think tracking who your users are, understanding what their needs are, and suggesting initial configuration for their dashboards or for their use of your software—that can work in every single niche. That can work in every single industry. I see a lot of cutting-edge founders building this into every single product they create, because it is always valuable to meet the customer immediately where they're at.
And this is easiest to do if you investigate where they're coming from. What information do I have about this person? How can I make it easiest for them to see where the value is?
Inside your product, you can also make AI a kind of transmission system between what the customer knows they want and how you need it presented to your database or your algorithms or your backend system. AI can communicate between these two because you can put in the effort to completely and correctly describe your system, and then task AI to translate between user requirements and your platform requirements.
That is another magical moment. It's the AI doing work for the user. It's almost an agentic thing. You have this little transmission process that an AI does for you as a user, and all you see is the data you want to get, and you just describe it with natural language.
This is the one way that I think every business can benefit from AI, and I highly recommend you implement it. This might even be a business idea, for that matter. So, you know, take a look at it.
The goal isn't to wow people with AI. The goal is to use AI to help people reach their own wow moments with your product. And when you do that invisibly, behind the scenes, creating those magical moments where everything just works exactly as they hoped—that's when AI becomes truly powerful for bootstrapped founders.
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