Atlas AI: AppTweak AI Model for the App Store & Google Play

Jonathan Frischby 
Head of Data Science

8 min read

With Atlas AI, AppTweak is transforming AI for mobile marketing. Just like ChatGPT, Atlas AI is a powerful deep-learning model. However, what makes Atlas AI different from ChatGPT and tools in other mobile marketing platforms is that it is exclusively trained on millions of data points from the App Store and Google Play.

Atlas AI provides AppTweak with an enhanced understanding of how the app stores work: how Apple and Google group different apps together, which keywords the stores consider relevant to certain apps, and so much more.

The unique insights Atlas AI delivers are unmatched in the industry. With Atlas AI, we empower mobile marketers to effectively navigate the app market using more relevant keyword suggestions, new competitor analyses, innovative keyword metrics, and more.

Learn more about AI features in AppTweak

Introducing Atlas AI to the world

Expert recommendations are everywhere across our platform – from our AI-generated keyword lists to competitor suggestions; our clients strongly rely on the quality of these recommendations in their day-to-day ASO operations. Maximizing AppTweak’s positive impact on your growth is our number 1 priority; we want our algorithms to be as precise and relevant as possible, which is why we were very proud to introduce Atlas AI to the industry.

Atlas AI allows us to enhance, develop, and preserve our high-level understanding of what keywords mean in the stores and how they match any given app.

Critically, Atlas AI has been built by and for ASO specialists. In an ASO context, we understand that the logic behind the store algorithms does not exactly correspond to how humans think in the real world.

The algorithms reside in an app store world, where words have different meanings, and where the connections between keywords and apps are based on ever-changing rules. We wanted to find a way to define these rules, mathematically analyze them, and be able to parallel the semantics of the stores.

Expert Tip

Let’s take the word “lime,” for example. In the real world, this keyword is closely related to the word “lemon.” However, in an app store context, these two keywords are very different: “lime” now refers to a popular scooter rental app, which is why the stores would consider “lime” to be more closely related to the keyword “scooter” than “lemon.”

In our quest to better understand this logic, we built one master language learning model, Atlas AI, that captures in one place the store-specific meanings of all keywords in any language, and all apps in any country or language, to position each element on one map.

Another motivation behind Atlas AI was our drive to create a foundational algorithm that would support the core of our key features. Delivering high-quality features is a key objective at AppTweak. A standardized understanding of what apps and keywords mean to Apple and Google allows us to more seamlessly deliver high-quality, valuable features to our customers.

The birth of Atlas AI

So how did Atlas AI come about?

First, we looked at organic rankings in live app store searches to infer meaningful relationships between apps and keywords. Every day since 2014, AppTweak has collected data on millions of keywords on the App Store and Google Play in about 100 countries. This huge amount of data feeds into our state-of-the-art natural language processing (NLP) models that we have optimized in an app store context.

Atlas AI uses 9+ years of AppTweak data to help you more easily navigate the app stores

Thanks to this data and our algorithms, Atlas AI was born. As a global map of the App Store and Google Play, Atlas AI positions tens of millions of keywords and apps relative to each other in a meaningful way that captures core app store semantics.

Since the data we collect is worldwide, Atlas AI differentiates keywords per language and differentiates apps per country and language. As a result of this, the keyword “candy crush” in English is naturally positioned in a different way than “candy crush” in French, as the respective meanings of each keyword are influenced by respective linguistic differences.

Similarly, the app Candy Crush Saga is also positioned differently in en-US (US English) than in es-US (US Spanish), fr-FR, or any other country/language, as the apps’ exact semantics change with linguistic and cultural nuances.

In a world of its own: Communicating with Atlas AI

So far, Atlas AI has correctly captured the meanings of keywords and apps – but it still speaks its own language. To communicate with Atlas AI and convert the mathematical formalism of semantics into tangible information, we use 3 types of algorithms and formulas – called services. These services are:

  • Distance computation
  • Recovery of nearest neighbors
  • Cluster identification
Service Goal Example
Distance computation Quantify the distance between pairs of elements: keyword-keyword, app-keyword, or app-app. Quantifying how relevant a specific keyword is to a specific app.
Recovery of nearest neighbors Retrieve the closest keywords to an app, closest keywords to another keyword, etc. Identifying the 1,000 keywords that are semantically closest to another given keyword.
Cluster identification Group apps that share a niche market, or group keywords that are almost synonymous. Identifying typos by pinpointing all keywords that share the same root meaning.

Although Atlas AI stores these app semantics per country and language, and keyword semantics per language, everything remains comparable. This means that Atlas AI can compare keywords across languages; for instance, it will easily confirm that “mon amour” and “my love” are very closely related, or can even find the most relevant French keywords for an app that has not yet been published in any French-speaking country.

Atlas AI fuels our mobile growth engine

Now that Atlas AI stores and can automatically compute the semantics of all apps and all keywords – and now that we can communicate with Atlas AI itself – we want to extract the most value from it.

To do this, we designed a three-layer architecture. Imagine this architecture as your grandma’s amazing lasagna (bear with us here):

  1. The base layer consists of our core component, Atlas AI, which mathematically captures the meanings of the store’s elements, apps, and keywords. It’s the equivalent of your grandma’s secret tomato sauce that makes her lasagne really stand out without your guests knowing exactly how.
  2. In the middle, we have the 3 services described above. They convert this mathematical formalism into tangible information, being distances, nearest neighbors, and clusters. These are the Italian lasagne pasta which ensures that the subtlety of the sauce becomes palpable enough to make you want to bite.
  3. At the top, we have the features: what our customers actually see. These are the refined outcomes of our 3 services, combining the raw results from Atlas AI with our industry expertise gained over the years. Obviously, you don’t make the best lasagna by just mixing the right ingredients in a bowl. You arrange them in the right way and present them with just the right touch of parmesan and a few extra fresh basil leaves, so your guests immediately understand the quality and value of their meal.
Turning Atlas AI into actionable ASO features and insights

With this three-layer architecture, we can easily develop new features without having to refer to the semantic foundations of the stores each time. Now, every new idea we have can be directly plugged into the “service” layer that provides the core analytics, all supported by Atlas AI.

To explain this in a more concrete way, let’s follow the three-layer architecture to suggest the right competitors for Trip Advisor in Italy-Italian:

  1. First, Atlas AI provides us with the semantic relationships of over 10,000 apps.
  2. Next, we use the “nearest neighbors” service to identify the 100 most relevant apps for Trip Advisor.
  3. Finally, we shortlist 10 competitor apps based on our unique criteria: App Power, diversity of suggestions, etc.

A bright future for Atlas AI

With Atlas AI, AppTweak is the only ASO tool able to benefit from an advanced engine that leverages the true meanings of keywords and apps, specifically in an app store context.

Having benchmarked Atlas AI’s results against pre-existing AppTweak algorithms, we have observed systematic gains in quality and speed. Thanks to this, we are able to continuously develop our algorithms across our ASO platform.

Atlas AI has also enabled the launch of unique AppTweak features, including our AI-generated keyword lists, the keyword relevancy score, and more. Moving forward, we will continue to capitalize on this enhanced understanding of the app stores to release more and more advanced features, unleashing the full power of Atlas AI.

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Jonathan Frisch
by , Head of Data Science
Jonathan is Head of Data Science at AppTweak. He worked as a data scientist in different industries before joining AppTweak in 2019, when the team was 5 times smaller than today. He is passionate about artificial intelligence in all its forms as much as dancing neo-tango.