ASO Atlas

Hi Fazier. I build iOS apps and got tired of ASO tools that guess search volume with opaque models, so I built ASO Atlas on Apple's own Search Ads demand data. It tells you how often a keyword is actually searched, how hard it is to rank for it, and which apps already own it, so you can pick terms you can realistically win instead of chasing head terms. It covers 60+ storefronts, tracks ranks daily, and ships a Mac app plus an MCP server so you can run keyword research straight from an AI assistant. There is a free tier that needs no App Store Connect login. Happy to answer anything about the data behind it.

Good Innovation bro https://www.b4sai.in/

Using real Apple Search Ads demand data instead of estimated volumes is the right call; guessed numbers are the main reason I stopped trusting most keyword tools. The MCP server is a nice touch too. Question on the opportunity score: how much does it weigh the strength of the apps already ranking versus raw search popularity? For a new app with few ratings, that balance decides whether a keyword is actually winnable.

For opportunity scoring across storefronts, how do you distinguish a low-competition keyword with little demand from one that is worth targeting? It would be helpful to see which signals drive the score and how they differ by storefront, especially when choosing between localized keywords.

For a new app with few ratings, that balance decides whether a keyword is actually winnable.It would be helpful to see which signals drive the score and how they differ by storefront, especially when choosing between localized keywords.

The Apple Search Ads demand-data angle is the right call — ASA popularity reflects real bid competition, unlike the scraped-frequency estimates most ASO tools ship. Two things I'd love to see: does the opportunity score factor in title/subtitle keyword density of the apps currently ranking, since that's the main lever a new app can act on this week? And any plan to track rank movement after metadata updates so wins can be attributed to specific changes?

Does the metadata optimizer flag words that are already in the title or subtitle when you fill the 100-character keyword field? Apple indexes all three together, so repeating a word there just burns characters. Catching that alone would pay for itself on a small app.

Congratulations on your launch

Scoring App Store keywords with real Apple Search Ads demand data across 60-plus storefronts, plus a Mac app and MCP server, makes opportunity picks less guesswork. If you ever need AI for the business, PZERO is at pzero.studio.

Ok looks good, with current level of cometiveness on app store this one could really help; where does it pull data from?

App Store Optimization is often tedious and hard to track manually. Having dedicated tooling around ASO data and keywords makes growth much easier. Clean product, wishing you a solid launch!

ASO is a tough problem, and you’ve done a great job.

Sourcing demand data from real Apple Search Ads instead of estimated volumes is the right call — guessed numbers are why a lot of keyword tools erode trust. The MCP server is a nice touch too. Genuine question: have you looked at tracking which apps get recommended inside AI assistant answers (ChatGPT/Perplexity "best app for X" queries)? App discovery seems to be slowly shifting there, and there is barely any tooling for it yet.

We ship a small iOS app, so the Take / Tight / Skip verdict is what caught my eye. Most keyword tools just hand you a difficulty number and leave the decision to you. Question: for a brand-new app with almost no ratings, does the difficulty score factor in your own app's current strength, or is it the same number for everyone? That would change a lot about what goes into the 30-character subtitle on a first release.

The 60+ storefront coverage and opportunity scoring look especially useful for deciding which terms are worth pursuing. How do you handle localization nuances when a keyword has strong demand in one market but different intent elsewhere?

We localize our iPhone app's listing into 21 languages, and keywords per locale are the hardest part to manage. Several storefronts index more than one localization (the US store also reads the Spanish (Mexico) keyword field, for example), so one locale's 100 characters can cover a second market. Does the opportunity score account for those extra localizations per storefront? Separate rank tracking for keywords assigned to Custom Product Pages would also be useful.

How do you estimate whether a keyword is winnable for a small app with few ratings? Is it based on the ratings count of the apps already ranking, or something else? And do you cover markets outside the US, for example localized keywords in Czech or German?