AI in Mobile App Development - Take Your Business to the Next Level with our Custom Applications!

[1] Personalization and Enhanced User Experience

The AI technology present in smartphones constantly monitors the user’s day-to-day activities, ranging from inspecting the user’s every Google search to consistently analyzing their social media activities. By leveraging AI technologies such as sentiment analysis & NLP, this data is processed to generate personalized recommendations. These recommendations effectively enhance the customer experience, improving customer satisfaction and ultimately customer retention rates.

The most noticeable use of this facility is observed in industries such as e-commerce & retail. Integrating a personalized recommendation module is a must-have for your business while developing your AI-powered apps if you want to retain more customers. It helps your business to classify its customers more effectively and allows you to advertise and market a specific product to people of the appropriate demographic more effectively.

[2] Process Automation and Improved Efficiency

AI in mobile app development can help you automate the routine tasks of your business that generally eat a large chunk of your employee’s time, let’s answer the “how” part with the help of an example. Suppose, you have a medical business and your employees have to process an ungodly amount of applications every day as there is a constant flow of applications. Additionally, all of the data has to be updated on the database manually which not only makes the process slower but also increases the risk of manual errors. Instead, you can implement an AI application that uses OCR technology to automatically extract and update the details of the patient to the database by using an identification token. This makes the entire process super efficient with absolutely no risk of errors from the employee’s side.

[3] Advanced Security Against Fraud Detection

The risk of fraudulent activities within a company’s operational setup haunts almost every industry out there. Statistically, the sinister shadow of this crime casts primarily upon the banking and finance industries. Users are always at risk of getting their accounts swept clean while transacting from a regular mobile application as they lack some important security protocols, which is why the implementation of AI in their mobile applications is so important. With AI mobile app development services, you can build an application with security features that can swiftly react to an inconsistent transaction from a user's account. The primary goal of this application is to verify if it’s the authorized user who made the transaction or a suspicious external agent trying to force their way into the system. An AI-based mobile app can identify and alert the user of fraudulent activity in 3 simple steps:

1. If the transaction amount or frequency suddenly changes from the conventional pattern, the AI further investigates the transaction.
2. It tracks the location from which the transaction was processed, now, if that’s outside the user’s regular locations as well.
3. The AI application flashes an alert to the authorized user’s device about a suspicious transaction. The user then has the option to verify if they made that transaction or not. In case the latter is chosen, the AI will push a request to the main server to temporarily pause the account until the issue is resolved.

[4] Advanced Search and Data Mining

Artificial intelligence in mobile app development allows users to get significantly better and more advanced search results as it allows the mobile application to understand the search intent based on the user’s preferences and browsing patterns. The AI-backed application can further elaborate on the user’s intent by using NLP to understand the requirements of a customer. This is achieved by analyzing the query to understand the context of the search request even if the main objective isn’t directly mentioned. For example, if a user is browsing an online clothing store and searches for “a pant with a lot of pockets”. The main keyword that the user is looking for is “cargo pants”, even though it is not mentioned here, the search results will show the user’s various cargo options as the AI model was able to understand the user’s intent behind the search. Additional features such as image searches and voice commands make finding the desired products a much simpler task, this further improving the experience of an AI-powered mobile app.

[A] Natural Language Processing

Natural Language Processing or NLP is popularly used in mobile applications that deal with a large amount of user-generated content. For example:

1. Social Media Applications: enforcing guidelines by detecting and removing inappropriate or spammy content by users is one of the many popular use cases of NLP in social media applications. Other use cases include sentiment analysis, content recommendation, etc.
2. Text Summarization Apps: NLP-based tools (like Summarizer) can quickly scan down extremely large bodies of text to create a gist of all the detailed points mentioned in the document, saving a substantial amount of the reader’s time.
3. Writing Assistants: While manually writing a piece of information, humans struggle to produce a perfect piece of writing with proper flow and appropriate use of words, especially in a time crunch. NLP-based tools can understand the reader's intent and recommend improvements in text in real time.

[B] Computer Vision

One of the most commonly used AI features used in mobile applications. Computer vision translates the visual world for your mobile applications and assists them in understanding the necessary visual components in their environment to perform necessary tasks:

1. Object Detection: the most common example of these features can be the facial recognition system of your phone that allows you to unlock your phone by just looking at it. Another example can be the filters that people use on social media apps.
2. Augmented/ Virtual Reality: computer vision is used in real-time image & video processing. This is the core technology used for popular services such as augmented reality and virtual reality which are used across several mobile applications.
3. QR Codes: this allows your mobile applications to translate QR codes which can be used in various operations such as payments, verification, and inventory tracking.

[C] Predictive Analysis

Now, let’s talk about the use of predictive analysis & machine learning in mobile apps. The primary objective behind using machine learning technology is to provide them with recommendations based on their using habits or assist them with their routine tasks such as:

1. Personalized Recommendations: e-commerce platforms and media apps use this feature to predict and showcase popular products based on your recent searches to help you choose the relevant product/service.
2. Health Tracking Features: ML is used in fitness applications to monitor the daily routine of a user and helps them keep track of their progress. Additionally, it can continuously monitor your vitals and predict any potential health risks.
3. Fraud Detection: machine learning is used in finance-based mobile applications to monitor the user’s purchasing habits and alert them in case of an unusual transaction, thus predicting fraudulent activities.

[D] Speech Recognition

The use of speech recognition AI in mobile app development provides users with a convenient and “hands-free” experience while using their smartphones:

1. Voice Control: speech recognition is used to control mobile devices without actually touching the handset, the AI-based system can understand the context of human language and perform actions based on the instructions.
2. Transcription Tools: this is much faster than typing or noting down a piece of information as the users can directly dictate a piece of information to the mobile application.
3. Real-Time Translation Tools: this is a game changer as it can automatically identify and translate the speech of any language, largely mitigating the language barrier. These tools can have additional features such as the option to mention the emotion behind the sentence and much more.