Top 10 AI app examples you should learn about

12 min read
September 25, 2023

AI app development is the best way to create a next-level app.

But, you need to have a clear idea about what AI can do for your app and how it can improve your users’ experience.

That’s why we’re going to discuss 10 top AI app examples that have successfully delivered value – they might give you guidance on developing your own AI app.

Let’s dive in!

Google Search BERT and MUM

Google Search needs no introduction.

But what you might not know is that the Google Search app is powered by 2 deep learning AI models.

The models are BERT (Bidirectional Encoder Representations from Transformers) and MUM (Multitask Unified Model).

BERT was introduced first and MUM was built on and expanded BERT’s capabilities.

These models work in the background and help improve the quality and relevance of Google’s search results.

They use natural language processing to better understand the context of each search query.

This helped Google better understand questions that contain prepositions like “to” and “for”.

Pictured below is an example of how it works:

source: Google Blog

Before BERT, the search query “2019 brazil traveler to usa need a visa” would show results about visa requirements for U.S. citizens traveling to Brazil.

This is because it mainly used keyword matching to show relevant results, which can’t understand context.

But, because BERT uses NLP and can understand context, the results improved and correctly showed results about U.S. visa requirements for Brazilians.

And Google didn’t stop there.

In 2021, they announced MUM, which can more accurately handle complex queries and more media types than just text.

It can understand:

  • Images
  • Videos
  • Audio files

This means that Google’s users can combine text searches and images to get even better results.

And that’s one of the reasons why Google is the undisputed market leader in their segment.

Netflix’s recommendation system

Neftlix’s recommendation system is one of the key reasons for its popularity and success.

The AI model in the app ensures that their users get the most relevant recommendations and a personalized experience.

And it works, too.

According to Netflix themselves, 80% of the content their users watch comes from their recommendations.

So, how did they make this happen?

The Netflix AI algorithm uses 3 main techniques:

  • User-based collaborative filtering
  • Item-based collaborative filtering
  • Content-based filtering

User-based collaborative filtering means that they analyze preferences and behaviors of similar users to recommend content.

So, if 2 users liked similar content, the algorithm will recommend titles that one user has watched and liked and the other hasn’t.

Netflix recommendations

source: Medium

On the other hand, item-based collaborative filtering uses user ratings to find similarities between two titles.

And content-based filtering recommends titles based on your preferences.

So, if you watch a lot of science fiction movies, Netflix will recommend other movies within that genre.

They combine these 3 techniques with deep learning models that analyze the vast amounts of data users generate.

And the end result of all this is an improved, personalized user experience (UX).

Nest Thermostat

Nest’s Thermostat is a great example of innovative use of AI in an app.

It uses AI to automatically adjust settings according to users’ preferences.

It’s also a good example of edge AI – the AI models are on-device and not on a cloud server.

But, what exactly does Nest’s AI model do?

Its main feature is that it can learn on its own.

The thermostat’s AI model learns from your habits and adjusts the temperature based on your preferences.

So, if you work from 9 to 5, it will adjust the temperature during that time in order to save energy.

Nest Thermostat UI

source: Cmple

It can also adapt to seasonal changes and optimize the temperature settings accordingly.

Another great feature is that it can integrate weather forecasts.

This means that it can make intelligent decisions about your heating and cooling needs several days in advance.

Also, the thermostat analyzes your energy usage and provides tips for energy savings in the monthly home report the app sends to users.

And all of these features wouldn’t be possible without AI.

Spotify’s Discover Weekly

Spotify is the world’s biggest music streaming service and you probably know about it already.

With over 489 million monthly active users and a 31% market share, it’s the undisputed market leader in its segment.

One of the reasons why it’s so successful is the AI they’ve integrated into their app with their Discover Weekly feature.


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Every Monday, Spotify’s Discover Weekly recommends 30 new songs they might like to each user.

Now, just think about the numbers involved for a second – that’s a total of 1 billion, 467 million unique song recommendations every week.

So, how do they do it?

Spotify uses three different techniques to deliver recommendations:

  • Collaborative filtering
  • Content-based filtering
  • Audio features

We’ve already discussed how collaborative and content-based filtering work and they work the same way in Spotify’s recommendations.

But, what truly sets Spotify apart is how they use audio features to recommend new music.

They use convolutional neural networks (CNNs) to extract musical features from raw audio files.

Normally, CNNs are used to analyze images but they modified their network to accept audio data.

They use them to create a sonic profile for each song, with the CNN’s output being key audio features like:

  • Key
  • Mode
  • Tempo
  • Loudness
  • Time signature

That data is then used to better personalize the recommendations in Discover Weekly.

And that level of AI-driven personalization is key to their success.


Grammarly is one of the most popular digital writing assistants on the market.

And the key to its success?

The AI model that underpins their app.

Grammarly doesn’t just correct typos and grammatical errors.

It also helps you:

  • Make your writing concise
  • Avoid overused words
  • Sound more confident

To do that, it uses:

  • Deep learning AI models
  • Natural language processing
  • Language rules and patterns

But, what separates Grammarly from regular spell-checkers is that it can understand context.

This means that it gives better, context-aware suggestions for improvement.

Grammarly UI

source: Trendhunter

Another great feature of its AI model is that it’s constantly updated with user feedback.

If a significant number of users click “ignore” on a particular suggestion, they update their algorithm to make it more accurate and helpful.

Also, you can adjust your style settings and Grammarly’s AI model will adapt its suggestions to your chosen style.

So, if you’re writing a formal e-mail, it’ll adjust its suggestions accordingly.

None of these features would be possible without their AI models.

Tesla Autopilot

Tesla has been at the forefront of AI development in the automotive industry.

It uses AI in a variety of ways, such as:

  • Battery management
  • Navigation
  • Autopilot

Autopilot is the most interesting and complex AI feature Tesla offers.

Autopilot can do a range of different tasks, including:

  • Lane keeping
  • Emergency braking
  • Adaptive cruise control
  • Summoning the car

But, how exactly does Autopilot’s AI model work?

It combines several different AI algorithms to power its driving assistance features.

Autopilot uses:

  • Concurrent neural networks
  • Recurrent neural networks
  • Reinforcement learning

The combination of these approaches enhances the performance of Autopilot’s features.

It uses the data it collects through cameras and radar to “see” the world around it and predict the future trajectory of pedestrians and other vehicles.

Tesla Autopilot

source: CNN Business

Then, it adjusts the car’s speed and the path it takes, if necessary.

Reinforcement learning is key to its success.

As the system collects more data, its performance improves.

This improves the overall performance and safety of the car.

Adobe Sensei

Adobe Sensei is a set of AI tools across Adobe’s many products.

So, while it’s not a standalone AI app, it’s still a great example of how AI can be used in a number of different apps.

Let’s start by looking at how it’s used in Adobe’s Document Cloud.

In the Document Cloud, Sensei powers its optical character recognition (OCR) and form recognition capabilities.

It can also auto-clean documents you’ve scanned and improve their readability.

And its form recognition capabilities make filling and digitally signing scanned documents easier.

Sensei also powers a number of features in Adobe’s Experience Cloud.

These include:

  • Predictive analytics
  • Personalization
  • Customer journeys

But, the most interesting and well-known AI-powered features are in Adobe’s Creative Cloud products like Photoshop’s content-aware fill.

Photoshop content-aware fill

source: Expert Photography

Content-aware fill makes removing unwanted elements from an image easy.

It analyzes the content of the image and automatically replaces removed elements with details from the surrounding areas.

Other great AI-powered features include Auto Reframe and Color Match in Adobe Premiere Pro and image tracing in Adobe Illustrator.

All of these features improve the capabilities and user experience of Adobe’s products.

And that’s what AI apps should be about.

Salesforce Einstein

Salesforce is the leading customer relationship management (CRM) app on the market.

And one of the reasons why they’ve kept that leading position is the comprehensive AI solution in the app, Salesforce Einstein.

Salesforce Einstein offers AI solutions for all of their products:

  • Sales
  • Marketing
  • Customer service
  • Commerce
  • Analytics

So, how exactly does it work?

The tools included in the Einstein platform allow you to build personalized AI assistants based on your specific business needs.

One such tool is Einstein Bots.

With it, you can build an AI chatbot that can help you and your team automate repetitive tasks.

Salesforce Einstein Bots

source: Salesforce Admins

For example, you can build a chatbot to handle the most common user questions and allow your customer service team to focus on solving more complex issues.

Another tool included in the Einstein platform is Einstein Discovery.

Einstein Discovery allows you to deploy an AI model to help automate your data analytics processes.

Another interesting AI tool on the Einstein platform is Einstein Next Best Action.

As the name suggests, it recommends the best action your team members should take in a given situation.

For example, the AI can recommend to which of your customers your sales team should offer discounts.

And we’ve barely scratched the surface of Einstein’s full set of features.

It just goes to show how many different uses AI has.

Amazon’s recommendation system

Amazon’s recommendation system is one of the most successful AI-powered recommendation systems in the world.

And that’s not just idle talk.

According to McKinsey, the recommendation system is responsible for generating 35% of Amazon’s yearly revenue.

Considering Amazon’s revenue in 2022 was $514 billion, that means it was responsible for $180 billion in revenue.

So, why is it so successful?

One reason is that Amazon tracks a lot of data about their customers.

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That data is then used to train their AI recommendation models and personalize each user’s shopping experience.

Another reason is Amazon’s long experience with AI recommendation systems.

The system itself uses the following techniques:

  • Item-based collaborative filtering
  • User-based collaborative filtering
  • Content-based filtering

You’ll notice that it uses the same techniques as Netflix and Spotify.

The thing is, Amazon pioneered item-based filtering back in 2003.

And they’ve been developing and upgrading it ever since.

That’s why their recommendation system is so successful.

Also, you can use it in your app.

Amazon offers the same technology underpinning their recommendation system through Amazon Personalize.

So, you can integrate one of the most powerful AI recommendation systems in your app without much trouble.

And that’ll help you take your app to the next level.

Bing Chat

Bing Chat is Bing’s chatbot, powered by OpenAI’s GPT-4 model.

As Bing hasn’t come close to dethroning Google, Bing Chat is their big bet on AI to tip the scales in their favor.

And it’s one of the best AI chatbot apps available on the market today.

So, what makes Bing Chat so good?

One reason is the power of the GPT-4 model it’s built on.

While it generates similar results to ChatGPT, it has a number of capabilities that ChatGPT doesn’t.

Bing chat

source: Computerworld

For starters, Bing Chat is integrated with the Bing search engine.

This means that it can access and search the internet and generate answers with up-to-date information, unlike ChatGPT which has a cut-off date of January 2022.

Another standout feature is that Bing Chat can also generate images as it’s integrated with DALL-E.

You can also upload your own image that you want more information about or that relates to your other prompts.

While it hasn’t (yet) increased Bing’s market share according to third-party analytics companies, Microsoft claims they’ve had strong growth from February 2023 onwards.

In any case, Bing Chat is an example of how AI can upgrade an existing app and give it a new lease on life.


Developing AI apps is going to become more and more common.

But, to get the most out of it, you need to do it right.

That’s why we’ve covered some top AI app examples that give value to organizations that use them.

To recap, they are:

  • Google Search’s BERT and MUM
  • Netflix’s recommendation system
  • Nest Thermostat
  • Spotify’s Discover Weekly
  • Grammarly
  • Tesla Autopilot
  • Adobe Sensei
  • Salesforce Einstein
  • Amazon’s recommendation system
  • Bing Chat

If you want to learn more, check out how we build AI-powered apps or read our article on the dos and don’ts of AI app development.

Written by

Karlo Mihanovic

Tech Advisor

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