Dear founder,
There’s something deeply satisfying about watching seeds you planted a year ago finally break through the soil. Figuratively, even though I do enjoy growing my own Tomatoes. But I digress. Today, I want to share a few stories from Podscan—my podcast intelligence platform—about what happens when long-term investments start compounding. Some of these took eighteen months to materialize. Others became possible only because I embraced tools I never thought I’d touch.
This is a bit of a year in review, though delayed and probably incomplete. But I think there are lessons here that apply to any founder playing the long game.
The Compound Effect of Programmatic SEO
Let me start with something that took real patience: our programmatic SEO efforts. For a solid year and a half, I wasn’t sure if they were working at all. Obviously, each new page—particularly if it comes with a .fm TLD like ours—has some work to do before it’s recognized as a trustworthy location.
But that seems to be happening now. More and more, people are linking to particular shows or particular episodes directly using the Podscan.fm link. And each of these backlinks from major newspapers like the Wall Street Journal, Forbes, and other places wherever a podcast is mentioned—each of these backlinks increases the quality of the domain. The domain rating goes up, and that obviously means better search result placements, higher trust value with anti-spam systems, and generally people picking our page to link to when they need to reference a podcast.
Here’s what I find particularly interesting: podcasters themselves are now putting links to their Podscan page in their show notes. Some of them have claimed their show on the platform. Others are just collecting links to every analytics service out there. Either way, the name has become almost synonymous with podcast analytics in certain circles.
This has proved to be a very reliable lead generator. More and more prospects find their way through what is effectively user-generated content—the podcast transcripts themselves. We just host this content, we facilitate already pre-generated content on the platform. For that reason, it’s a very cheap way of generating unique content. And because it’s genuinely useful content, it’s found by people with vested interest in that particular topic. They discover Podscan, they explore other features, and ideally, they become customers.
Competitors in this field have understood this too and have similarly high-rated domains, just from all the links they’ve been able to collect over a longer time than Podscan has been around. That comes with the territory of being a newcomer. But the compound effects are real, and they extend beyond just search rankings. Domain age helps with email deliverability scores too. The reputation builds in multiple dimensions simultaneously.
Better Data Through Open Standards
Another investment that’s been paying off is our integration with OP3—an open standard for transparent podcast analytics. The idea behind OP3 is simple: it can be used as a prefix for the URL to each file that’s being downloaded by a podcast client. Then OP3 collects metrics about how often people download episodes, how many of those people are bots versus real users, where they come from, when they download, how much of the file is downloaded—all this information.
We’ve built an integration that synchronizes OP3 data with our existing database. Relatively few podcasts actually use OP3, but the ones that do obviously benefit from us showing their real tracked analytics on their Podscan page.
But here’s where it gets interesting. We’ve been collecting so much data through OP3 that we’ve started feeding this data into all the machine learning and estimation models we use for audience sizes and download numbers. So for podcasts that may not have as easily traceable information, our estimates are now more accurate because they’re calibrated against real data.
This integration benefits the user—they see real data for real podcasts—and it benefits the platform’s data fidelity overall. It helps stabilize our estimates across the board. That kind of improvement takes time to materialize, but once it does, it compounds.
The Query Language I Never Would Have Touched
Now here’s a story that really captures something about how my work has changed over the past year.
About six months ago, we migrated our search system. We moved from a setup that included a lot of hand-crafted Laravel code and Meilisearch—which, I should say, is still a very great search engine for full-text search—to an OpenSearch cluster maintained and scaled autonomously on AWS.
This migration had a tradeoff. Meilisearch was incredibly fast. We’d sometimes get results back in under three-digit milliseconds—a fraction of a second. OpenSearch is still very fast, definitely sub-second, but not quite as lightning-quick.
What we gained, though, was reliability and capability. OpenSearch is such a well-tested, well-established system. It can handle a lot of data really well. And because it has the Elasticsearch query DSL—the domain-specific language for expressing search queries—it’s highly configurable. We can get exactly the right results in exactly the right order for any possible request.
Here’s the thing, though. Two years ago, I would never have touched this.
I was burned by the complexity of Elasticsearch a decade ago. It always freaked me out—such a complex, hard-to-compose way of querying a database. That’s actually one of the reasons I initially went with Meilisearch. It had more of an HTTP-parameter style of search instead of deep, nested JSON objects.
But what I found was that now we have very capable agentic coding agents. I can just tell the agent what I want the query to do, what I want the results to look like. And the agent—having been trained on millions of documents containing Elasticsearch and OpenSearch queries, understanding what they do and what the results look like—is so much more capable than I am of crafting not just the right query, but the logic that would compose such a query reliably and testably.
This has been a game-changer. I’ve been a big fan of agentic coding—just listen to the last thirty or forty episodes of this podcast if you want evidence of that. But the quality of these systems isn’t just in the code they write. It’s also in the code I don’t have to write. And maybe more importantly, their actual value is in the code I would never have written, or could never have written, or never would have wanted to write.
That particular hindrance of mine—where I wouldn’t even have touched Elasticsearch—would have kept me from building a system that now runs extremely smoothly, extremely reliably, and is highly customizable. It powers not only search but also internal reporting and analysis across the platform.
I just wouldn’t have done it if I hadn’t been able to tell the agent to do it for me.
Big, big margin improvement over what the years before would have been possible.
The migration also gave me a reason to rework the filter and search interface, which has been received very positively. It’s more of a professional tool now than a quick lookup tool. If I ever want to build a simpler quick-lookup experience, I can layer that on top with simple queries and a solid caching system. Very nice.
The 10-80-10 Automation Model
Outside of search, I’ve been building a lot of what I call semi-automated systems. I’ve talked about this kind of automation before: the first 10% is me, then there’s 80% AI, and then the last 10% is me as well.
These systems are part of my preparation for being less hands-on in the day-to-day operations. You know how it is—every founder wants to be able to focus on things that take their full attention, not just write emails or sift through databases and records.
The most helpful one so far has been a targeted mid-trial AI-drafted outreach email. It combines data about what the user has already accomplished in the application—the things they’ve seen and tried—and kind of congratulates them on having done this, plus gives them the highest-impact next step to take. It’s been quite useful for giving people insight into the full platform capabilities.
I’m building a lot of similar systems: AI-assisted scraping, data lookup, validation, confirmation. The GPT systems have gotten so good that they can do a reliable 80% of the work I used to do when it comes to data acquisition and verification.
If you’re interested in hearing more about which of these systems have worked and which way, I can dive into the details in a future episode. Just let me know.
The Compound Effect
So that’s the picture. Long-term investments in SEO are compounding. Data quality is improving through integrations like OP3 and AI-assisted validation. Agentic coding has unlocked capabilities I never would have attempted on my own. And semi-automated systems are freeing up my time for higher-leverage work.
It’s been a good year for Podscan’s growth and data fidelity. What strikes me most is how these improvements feed into each other. Better data makes better search results. Better search results make happier users. Happier users create more backlinks. More backlinks improve domain authority. And the cycle continues.
Patience, it turns out, is a competitive advantage. So is being willing to embrace tools that let you build things you never thought you could.
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