Why I Built ASAPilot

I manage 20+ apps on the App Store. Apple Search Ads was eating me alive. So I built my own fix.

Let me paint you a picture.

Twenty-something apps. All on the App Store. All running Apple Search Ads.

Every morning I'd open my laptop and start the ritual: log into the dashboard, click through each app one by one, check which keywords were converting, which were bleeding, export a CSV here, cross-reference a date range there. On a good day this took an hour. On a bad day — the kind where something had quietly gone wrong overnight — it took most of the morning.

And the worst part? Even after all that time, I'd still miss things.

I'd check App #7, move on to App #8, and by the time I finished App #20, I'd have forgotten what I saw in App #7. I was holding too much in my head. The operation was too spread out for any human to track manually without dropping something.

I dropped things constantly. Money-shaped things.

The moment that broke me

I don't remember exactly which app it was. What I remember is opening the dashboard one morning and seeing a keyword I'd set up weeks ago — a broad match I'd meant to review — had been running at full spend the entire time, with near-zero conversions.

Not a huge amount of money in absolute terms. But money I had chosen to waste by not watching closely enough. That distinction matters when you're a solo developer and every dollar of ad spend is coming directly out of your pocket.

I sat there doing the math on how many times this had probably happened across 20+ apps without me noticing. The number was uncomfortable.

That afternoon I stopped doing client work and started writing a script.

Building for myself first

I wasn't thinking about a product. I was thinking about solving my own problem before it cost me more money.

The idea was simple: pull all my Apple Search Ads data through the API, run it through an AI, and get a plain-language summary of what was working and what wasn't — across all my apps at once, every morning, before I'd had my first coffee.

The first version was rough. But the first morning it ran and flagged three underperforming keywords across two different apps that I hadn't caught myself — I felt something I hadn't felt in months.

Relief.

Not excitement. Not "this could be a business." Just pure, uncomplicated relief that something was finally watching the accounts so I didn't have to watch them every second.

What surprised me most

I expected the tool to help me cut waste. It did — ad costs dropped meaningfully once I had a systematic way to catch dead keywords before they ran too long.

What I didn't expect was what it surfaced on the other side: the good signals I'd been missing.

When you're managing 20 apps manually, you optimize for catching fires. You don't have the bandwidth to notice the quiet wins — the keyword in a niche category that's converting at half your target CPA, the search term you never thought to bid on that's actually driving installs.

The AI started flagging those too. And that's where it got genuinely interesting.

Some of my best-performing keywords today came from the tool noticing a pattern I'd never have spotted manually across that many campaigns. Vertical-specific terms, long-tail phrases, combinations that looked weird in isolation but made sense in context.

The tool didn't just stop me from losing money. It started helping me find money I didn't know was there.

The moment I realized it wasn't just for me

I mentioned the tool to another developer — someone managing maybe 8 apps, smaller operation than mine. He asked if he could try it.

A week later he sent me a message: "I caught something on day two that would have cost me $600 by end of month. Is this thing for sale?"

I hadn't thought about selling it. It was just a tool I built for myself because I was desperate.

But I started asking around. Turns out the problem I had — too many apps, too much data, too little time, too many things falling through the cracks — wasn't unique to me. It's just the reality of running multiple apps on Apple Search Ads without a team.

That's when ASAPilot became a product.

What it looks like now

ASAPilot connects to your Apple Search Ads account via API, monitors your campaigns continuously, and surfaces the things that matter — budget waste, keyword anomalies, CPI spikes, and the quieter signals like high-converting terms worth doubling down on.

It's intentionally read-only. It doesn't touch your campaigns. It watches, analyzes, and tells you what it sees. You make the calls.

I kept it read-only partly for trust reasons — nobody wants a tool that can accidentally pause their campaigns — but also because that's how I use it myself. I want to be the one making decisions. I just want to make them with better information, faster.

We've now got 100+ accounts connected across the user base, and we've helped surface over $1.2M in identified waste. Most users see their first meaningful catch within the first week.

I'm still a solo developer. I still run 20+ apps. The difference is I don't dread Monday mornings anymore.

What I'd tell someone in the same situation

If you're managing more than a handful of apps and doing Apple Search Ads monitoring manually, you are losing money. Not because you're bad at it. Because it's genuinely impossible to do well at that scale without systematic help.

Build the systematic help. Or use someone else's.

The hours I spent staring at CSVs weren't giving me better results. They were just making me feel like I was in control. There's a difference.