Hey, I'm Andrew, founder of Saga Solve, and this is our go-to-market strategy and tactics for the AI era. The goal here isn't a launch-day splash — it's building a repeatable system that finds the right customers, pays them to tell us the truth about our product, and turns that feedback into shipped changes fast.
Who we're selling to
We've already identified our ideal customer profile: a senior marketer with heavy SEO experience at an independent or small agency. The next step is to find those people and then show them the product — because we already know the right ones like it, love it, and are willing to pay for it. Everything downstream exists to put the product in front of that profile and learn from what they do.
The qualifying funnel
We bring people in from ads and cold outreach on LinkedIn, offering an hourly payment, and funnel them to a product interview page. That page carries a set of qualifying questions to filter for who we're actually looking for — including making sure people understand they're getting paid, since we're only paying so much per interview and some candidates have very high hourly rates.
On the page we collect name, email, LinkedIn, role and location, plus sentiment on AI and agentic experience. Agentic experience matters a lot for us to know. One thing we're testing is the framing itself — possibly switching a question to "what's your pain point?" — because people do sign up without really knowing what the interview is, and a screen-share or explanation is needed.
We also expect most people to have no experience with agents yet; agents are new, and our product is ahead of its time. That's exactly why the interview page is built to qualify rather than assume.
Scheduling, no-shows and click fraud
After a lead qualifies, we send the actual calendar link by email. That avoids bots — there's a lot of click fraud out there, and we don't want to waste our time on it.
The second problem is calendar hygiene: people book and then don't show. Only about a third of bookings show up, which is wild for a paid interview. So we follow up, check whether they're still interested, send reminders, and if they don't respond, we simply remove them from the calendar. Tracking drop-off across this funnel is the critical metric — if the qualifying page is too long, it causes drop-off, and we adjust it accordingly.
Inside the human interview
Once people are in a meeting, we run a very clear structure: walk through the landing page component by component, sign in, go through the onboarding flow with questions at each step, then the agent experience. The goal is to get people to the magic moment really fast — and to learn whether we're communicating the right things, because agents are very new and it's genuinely tricky to communicate their power.
Interviewers follow a script and document everything the participant says. There's also a deliberate call about what to trim versus keep — information only matters if it leads somewhere.
One filter worth knowing: some people are using the interview as a selling opportunity. When you ask for feedback and get none, the instruction is to end the interview immediately and go back to being productive. They want a paycheck to sell to you, and you have to filter those people out.
The referral flywheel
When an interview ends in good faith, we have a procedural ad generator waiting. It creates a personalized ad for that person to post, and the payment comes with a referral link — so if anybody signs up through it, they're compensated too. They can post and share the ad on social media if they choose, giving them both the payment and upside on referrals.
The hope is a flywheel: come for the product interview, and since we can create a customer ad at extremely low cost, every interview can also seed new outreach to more SEOs. More interviews mean more information, and more participants mean more ads out in the world.
The AI research-to-action loop
This is where the power of AI comes in. The human review is extracted in a very structured form — which is why interviewers follow the script. AI takes that and generates a report per component of the landing page, plus a summary. Once we've piled up about five of these, AI aggregates them into a single report, and a meeting gets scheduled for the human review, where we decide what to change and update.
Once decisions are made, they go out to Jira. And because these processes are so well defined — and the agents are so good now — micro agents can make the changes for us, which we review before anything goes out. They change the landing page, the onboarding experience, the agent experience, and even this workflow itself.
That's the whole loop: use AI to aggregate, structure and prepare information so we can make decisions very quickly, and let coding agents keep the product highly adaptable. The loop feeds back into itself — we adjust the interviews, the ads, the outreach and the qualifying page based on what we learn.
That is the product UX interview process we're using to polish the product for organic conversions. Next in the go-to-market series: our landing page, the social post technology used to make these videos and get them distributed, and the agent experience itself.
Transcript
Hey, I'm Andrew, founder of Saga Solve, and this is our go to market strategy and tactics for the AI Era.
Alright, so we've identified our ideal customer profile. Uh, they are a senior marketer with heavy SEO experience at a independent or small agency. And so now we have to find those people and then show them, because we know that they like our product, they love our product, and they are willing to pay for it.
Alright, so I'm going to go over the qualifying funnel. Uh, we're bringing people in from ads and cold outreach on LinkedIn and offering, uh, the hourly payment, and it funnels them to this page for the product interview. And it has a number of qualifying questions. Uh, and so this is just to filter out who we're looking for, make sure that, you know, people understand they're getting, uh, paid, because obviously, you know, we're only paying so much for, uh, this interview. Um, and some people have very, very high hourly rates. And so these are just all the questions that we ask. And then we collect sentiment and, um, agentic experience. That's, uh, very important for us to know. Now, this is very important for us because we, uh, are really getting interviews for the funnel.
So uh, after the leads are qualified, what we do is we make sure, uh, to send them an email, like the actual calendar link, uh, in an email. Because we want to avoid, uh, bots. There's a lot of click fraud, uh, so we don't want to waste our uh, time. And then we also check. We have another problem. Uh, we only have so much time in the day. People, uh, book and then they don't show. So what we want to do is we want to actually remove those people from the calendar. It's actually a pretty big problem because people will book and maybe about a third show, um, which is crazy. It's a paid interview, but it's wild. Um, so, uh, basically we follow up, we just see if, hey, they're still interested, send some reminders, and if they don't respond, we just remove them, uh, from the calendar. And then, so, uh, once we get those people into a meeting, uh, we basically, we have a very clear structure of going through our uh, landing page, uh, every component there, uh, signing in, going through the onboarding flow, checking, asking questions about that, um, and then, uh, the agent, the uh, agent experience, the firsthand agent experience. Because we have to get people to the magic moment really fast. And so this is going to give us the information of how we clean up our landing, how we clean up our onboarding step. Like, are we communicating the right things? Because agents are very new. We are um, definitely, you know, ahead of our time here, as arrogant as that sounds. Um, but it's, it's really tricky to communicate the absolute power of agents. Uh, and then we end the interview once we get that information. And what's very important here is, uh, what I've noticed is some people are using this as looking to sell in opportunities. And um, you know, when that's. They're. They're not really interested in giving you information. So when you ask for feedback and you don't get it, the instructions are just to immediately end the interview and then go back to being productive on something else because, you know, they're not interested. They're uh, they want to take a paycheck to sell to you. And so you have to, you have to filter through those people.
Now, uh, what we want to do to get a flywheel up here when the uh, when it ends and you know that they participate in good faith, uh, we have the ad generator, we have a procedural ad generator, um, up here. And uh, what we can do is we can generate a personalized ad for somebody to post, uh, so we pay them and then it comes in with a referral link as well. So they have an incentive. If anybody signs up from this referral link, um, they're going to be getting compensated, um, so they can post and share an ad on social media, um, if they so choose. So we're hoping to leverage this into like a flywheel effect that hey, come for this product interview. We're looking for people that are uh, SEOs. They basically just uh, we create a customer add because we have the technology to basically do that at extremely low cost. Um, so we want to plug uh, in the ad generator, uh, down here.
So what we're going to go into next is actual the power of AI. So we have these. Our human review is, uh, extracted. It's pretty structured. Uh, it's very important that, um, the interviewers follow this script because we use AI to actually, uh, generate, uh, structured information of our landing. So, um, AI will take it, it will generate a report, like per component, and then a summary. And then once we pile up five of these, we're going to aggregate a report. We use AI to do that. Um, and then a meeting gets scheduled, and then we go into the human review. And then we basically, uh, review the information that we're finding. And then we decide what we want to change and what we want to update. So this is where the human, um, review comes in. So we want to use AI to aggregate and structure and prepare our information so we can quickly make, uh, decisions. And once those decisions are made, they go out to jira. And because these processes are so well defined, uh, you can. And the agents are so good. Now we can have these microagents, um, change what we want, uh, for us, and then we can review the changes before they go out. And, uh, also we, we get information on this entire process. So not only do agents, um, change our landing, our agent experience, uh, our onboarding experience, they will also change this workflow here. And so this is everything that AI's very good at, which you can see is getting us information so we can make, uh, decisions very quickly. And now the agents are so good at coding, they can, um, we can make our product, uh, very adaptable.
So, uh, that is the product ux, um, interview process, uh, that we're using to polish our product to get it ready for um, organic conversions essentially. So we're getting a lot of information and then next time we're going to go over what we're doing on our landing, um, our social posts, like the technology that I'm using to make this video actually and uh, get it distributed and then also the agent experience of what's involved there. So those videos will be coming next in our go to market series. All right, peace guys.
Frequently Asked Questions
It offers an hourly payment to senior marketers with heavy SEO experience at small or independent agencies, who then run through a scripted product UX interview.
Because of click fraud and no-shows. The link is sent by email to avoid bots, and unresponsive bookings are removed since only about a third of people who book actually show up.
AI generates a report per landing page component from each interview, aggregates roughly five interviews into one report, and the team then decides what to change before it goes to Jira.
Micro agents make the small changes, and the team reviews every change before it goes live.