How to Integrate AI Into an App

This article is dedicated to giving you a general overview of how to integrate AI into an app. We’re going to cover an in-depth process of AI implementation and give you a framework with a step-by-step guide on how to offer AI-powered features for your users tomorrow. Here you’ll find everything needed to get on top of your AI game in no time and within a very reasonable budget.

Existek has been working on custom software development projects since 2012. Of course, we’ve been participating in the AI race in recent years. Many of our customers want to integrate AI with existing apps we’ve built for them or come to us with the need to create a new product around AI.

Take a look at the Aibly case study, a prime example of an AI-based healthcare application we’ve developed. Our primary objective was to embed AI at the app’s core, enabling it to recognize speech patterns and assess the mood of patients, thereby facilitating immediate assistance from therapists.

We recently released another product that uses Google’s Vision AI to find and DMCA-claim copyright-protected images across the Internet. 

Our team is not a stranger to AI integration and implementation, and we’re glad to share our hands-on experience, which can help you boost your customer’s experience and add AI-powered features to your app.

The list of the Content

• How to integrate AI into an app?
• How much does it cost to integrate AI into an app?
• How to use AI in an app: examples of AI integrations and providers’ fees
• Challenges of AI integration
• Reasons to integrate AI into your app
• Conclusion

HOW TO INTEGRATE AI INTO AN APP?

AI is booming now. Besides offering their AI products to end users, many companies provide other businesses with handy methods for using their AI components in other products. 

If you have favorite AI tools that you use daily today – check their websites, and you’re likely to find sections dedicated to the integrations. 

For instance, OpenAI’s integration section has numerous examples of companies that have integrated with their GPT model API and started using the most advanced AI immediately. JetBrains case is very prominent. This company’s software development tools products are used by more than 15 million developers worldwide. JetBrains is excellent in their own Integrated Development Environments domain, which they explored and enhanced over the last 23 years. But they’re not an AI company; they didn’t have their own artificial intelligence model or even dedicated specialists to work on it. However, they saw great potential that AI could add to their product. So how does the company, even with enormous resources like JetBrains, catch up with the AI boom in a timely manner? The answer is simple: partner with OpenAI to get the most advanced model as a part of their own product without spending billions of dollars and years spent reinventing the wheel. 

If you’re looking for a way how to integrate AI seamlessly into your project – the extensive availability of ready-made solutions makes it more accessible than you could ever imagine.

The ready-made options refer to GPT chatbots, NLP solutions, image, text, or voice recognition, personal assistants, generation solutions, copilots for sales or security, translation, task automation, generalization, and so on. Incorporating the desired AI-powered feature into your application only requires partnering with the right AI service providers.

These pre-built components provide not only the required functionality but also significantly streamline development processes, reduce costs, and accelerate time to market. Teams can now focus more on customization and innovation.

Let’s help you define the essential steps on how to add AI to your app. We always encourage the structural approach and guide our customers at every stage, from product discovery to final deployment. That enhances efficiency, minimizes risks, and enables our customers to implement reliable and feasible AI integrations. 

So, what are the main steps to integrate AI?

Step 1. Analyze the feature list of your application

Stakeholders involved: Product Owner, Project Managers, AI Consultants

If you think AI is just for solutions with super innovative concepts, it’s time to look from a different perspective. Would you find at least one existing functionality in your app to improve? Sure thing, there is always something to add and enhance user experience. The point is that when analyzing specific features and prioritizing your needs, you can determine how AI could be used in your app. For example, your product needs an extra tool to provide best-practice answers for better customer support, analyze visual data for detecting objects, or localize content for global users. These are exactly the use cases when it comes to AI integration, as the market is already full of ready-made solutions.

Step 2. Define the use case for integrating AI

Stakeholders involved: Project Managers, AI Specialists, DevOps Engineers

AI functionality extends beyond what we could consider. It evolves with every new use case and will be easily adapted to the specific needs of your project. Looking at particular app features, you can hardly guess if it was made from scratch or just integrated to be customized within this application. Why not incorporate AI in your app without considerable effort? 

Depending on the problem’s nature and desired outcomes, the team starts to search for pre-made AI solutions at this stage. It is a common practice to refer to leading AI service providers such as OpenAI to check for possible off-the-shelf solutions. Continuing with the sampled above needs, the team could use ChatGPT API integration to introduce the AI chatbot, Google Cloud Vision API to detect objects from images, and Amazon Translate to add high-quality real-time translations.

Comprehensive research of several available market alternatives helps finalize technical requirements and validate the effectiveness of the chosen solution. Another important task is checking the providers’ fees for offered services and estimating the required budget to use the chosen tools. We’ll go into more detail in the section on how much AI integration costs.

Step 3.  Ensure the availability of required data

Stakeholders involved: Data Engineers, AI Specialists, Domain Experts

Before investing in new data infrastructure, it is crucial to maximize the value of your existing data. Unless you are new to the market, you definitely have a huge, valuable data set gathered over the years of providing services and products to your customers. And it won’t be surprising to put it at the core of successful integration. Many AI models are subject to continuous training, thus supporting better functionality in the end. The large datasets are ready for AI consumption when they are clear, labeled, properly formatted, and segmented. If some data originates from external sources, the team has to be mindful of data usage ethics and legal compliance.

To prepare training data for the AI chatbot, the team has to gather available types of data from different resources, clean and organize it for further usage. When using Google Cloud Vision API, make sure that your images have the supported file formats and don’t exceed 20MB. With Amazon Translate, you might need to determine how specific terms are translated, including your branding words, the level of formality, or define the overall style of your content. The good news is that most of these tasks can be guided by the AI providers to streamline this integration step.

Step 4. Start AI integration and development

Stakeholders involved: Developers, AI Specialists, DevOps Engineers, UI/UX Designers, QA Engineers 

That is a core stage where the technical part begins to embrace the smooth adjustment of chosen AI functionality within your app. It’s worth all the effort to combine the efficiency of established technologies with the opportunities for customization. Of course, any ready-made tool comes with detailed documentation to help teams streamline the successful completion of required tasks. They take primary responsibility for tuning the chosen integration according to project requirements and successfully completing AI-powered features.

API Development: The API acts as a bridge to leverage the AI capabilities within the existing app architecture. It covers the preparation steps for obtaining API keys from your selected providers and creating interfaces that interact with AI services. All the detailed API information is covered in the documentation, such as OpenAI API Reference, Cloud Vision API Reference, and Translate API Reference. Following all the necessary steps, developers approach configuring server-side functionality and adding design elements like buttons or text fields for embedded AI. 

Backend Integration: It’s obvious that the backend needs to be configured to communicate with the AI services or components. It’s time to bring AI functionalities into the app’s backend infrastructure. This involves setting up API calls, data pipelines, and model deployment processes. For instance, when using a cloud-based AI service, the backend would handle API requests, send data to the service, and process the AI-generated responses.  Any ready-made tool comes with step-by-step instructions given by the AI service providers (OpenAI Chat Completions, Cloud Vision API Guides, Amazon Translate Developer Guide) to help teams streamline the integration on the server side of your app. 

Front-end Development: Product managers, together with designers and front-end developers, also accommodate new AI features. They create workflows, UI concepts, and mockups for the front-end developers to do their jobs. This is an easy step because there is no other way but to follow the standard industry best practices for UI/UX. 

For instance, whether AI-powered or not, chatboxes will look exactly the same. A simple embedded code with the default chat bubble is enough to call an AI-enabled app. If there is a regulatory need – the chatbox might have some disclaimer about the AI-generated responses, and that’s it.

Of course, artificial intelligence solution providers offer many front-end customization options. You can use one of their templates, modify it, or not use a template at all. However, the development and product teams can always go the extra step in creating a custom front end to maintain the integrity of the application’s interface.

Testing: The team can’t omit rigorous testing of the implemented functionality. It involves systematically evaluating all components together to ensure they work seamlessly, maintain stability, and perform efficiently under various conditions. This vital step helps identify and resolve any issues that could impact the app’s reliability or performance before it is deployed.

Step 5. Undergo user testing and feedback 

Stakeholders involved: Product Managers, UX/UI Designers, Developers, QA Engineers

It’s important to conduct usability testing as early as possible to observe users’ behavior and enhance it based on early feedback. The primary purpose is to detect any potential issues, gather insights, and make adjustments before it goes into production. 

Beta Testing: It is one of the best approaches to uncover missed bugs, assess performance, and gauge usability. Moreover, it’s conducted with real users to get direct feedback. The critical areas of beta testing embrace technical stability, real-world adaptability, performance, localization nuances, etc.  

Feedback Collection: Conducting user tests provides you with invaluable feedback, eliminating the guesswork about why something failed and even the need to start from scratch. Actually, the point is that the gathered feedback helps to reduce the risks of significant revisions and enhance user satisfaction. Continuing with our sampled use cases, the team could check how happy users are with the chatbot’s response time and whether the given responses are relevant, accurate, and contextually appropriate for your app’s context. They could also get the list of frequent errors with object detection accuracy or evaluate the quality of translating some specific languages.

Iteration: This iterative approach ensures that AI-powered functionality evolves to better meet user needs and achieve optimal performance. Sometimes, it might require only minor adjustments, like adding an extra button or changing a few optimization parameters. Thus, the team is confident that they have reached the desired performance and presented the AI functionality as intended.

Step 6. Deploy the AI integration and continue monitoring

Stakeholders involved: DevOps Engineers, Developers, AI Specialists

With your AI-enhanced feature ready for action, launching it into the real world is time. But the job doesn’t end there – ongoing monitoring is essential. By closely monitoring how the new functionality performs, you can ensure it adapts smoothly to live data, spot any issues early on, and fine-tune the system to keep it running at its best.

Deployment: Before going live, final checks are performed to verify that everything is functioning as expected, minimizing risks and ensuring a smooth launch. This is the long-awaited step when your team can finally transition the implemented feature from a development or staging environment to a live production environment. 

Monitoring: Of course, your team will continue to be in charge of regularly checking how well the app runs and how accurately the AI-based component functions. Staying on top of these elements can quickly address any issues, ensuring the app remains efficient, reliable, and responsive to user needs. It’s about keeping the app in top shape and providing all the functionalities to perform at its best.

Maintenance: A truly engaging and smooth user experience within your app can be gained through continuous improvement and updates. To fully harness the power of AI solutions, the team often trains them according to product needs by fine-tuning them on your specific data. Besides addressing any technical issues, it could involve updating the pre-trained model with additional training on a smaller, domain-specific dataset. This customization improves the AI’s performance in your particular application by leveraging its existing capabilities while adapting it to understand and respond to your unique requirements. Fine-tuning ensures the AI tool is more accurate and relevant to your specific tasks, like customizing the response language to reflect the brand’s personality, interpreting medical images for treatment planning, or supporting the user’s personalized vocabulary.

Integrating ready-made AI solutions can be a transformative move for businesses, offering a powerful blend of efficiency, innovation, and of course cost-effectiveness. As we explored, thoughtful planning and right execution help in fine-tuning the AI’s performance and maximizing its benefits for your business.

HOW MUCH DOES IT COST TO INTEGRATE AI INTO AN APP?

Exploring the vast AI field involves a slightly different approach to estimating the overall integration cost. The decisive aspect is that these costs combine external payments to the AI service providers and internal R&D. When the expenses for research and development cover the developers’ work on the integration process, you also incur expenses of using the third-party tools. Let’s get into more details and get some estimates based on the pricing as of 2024. 

The third-party tool’s operating cost

AI integration cost is commonly split into Development cost and Operating cost.

Development cost can start with a very small amount and up to millions of dollars – depending on the complexity of new AI-powered features. This is the cost of actually inserting new functionality into an app, and usually, this is a one-time investment. 

Operating cost is the monthly fee you pay to the AI company for using its resources and services.

At first sight, it seems complicated as each AI cost model is determined by specific usage requirements. For example, active custom translation with Amazon Translate is charged based on the number of used characters ($60,00 per million characters), text prediction cost with Vertex AI is calculated by used document pages ($25 per 1,000 document pages), or OpenAI API pricing is calculated based on tokens ($5 per 1M input tokens and $15 per 1M output tokens with the GPT-4o model – input tokens refer to the text or data you provide for an AI model, while output tokens are the responses or generated text produced by model).

Therefore, usage charges from different AI providers vary based on the volume of requests or data processed, infrastructure costs for hosting and scaling the AI services, and additional expenses for accessing extended features. All the pricing details can be found on the official website of your chosen third-party provider. Besides, they always offer calculators like the AWS pricing calculator, or your team can request custom quotes outlining your specific needs. 

OpenAI pricing embraces models for different capabilities and prices in accordance with actual needs. The monthly budget for the chat feature with OpenAI API could start from $100 and increase with your rising needs. OpenAI offers both free and paid options for accessing its API. They’re varied by limit rates, such as requests per minute, requests per day, tokens per minute, tokens per day, and images per limit. Based on the API usage rates, they automatically specify your usage tier.

Tracking monthly expenses and setting a budget limit can be a reasonable solution to avoid unnecessary spending and manage operating costs. 

Integration (Development) cost

The other group of expenses is associated with the development process itself to integrate AI into an app. It’s obvious that the company has to engage different specialists to work on integration and often customization to align with the app’s vision. Moreover, the team can make further adjustments and train the AI model on your business-specific datasets.

A middle developer will spend around a day inserting the chatbot into the application and doing some testing. But we’re bundling something more complex, and the integrated functionality just serves as a starting point for dealing with more sophisticated tasks. 

Even the simple chat feature can be significantly advanced when customized to specific needs. It’s essential to produce valid content of immediate expertise and domain-specific user queries. For example, we worked on the AI chat for WeConciile, the relationship restoration app. The team integrated the vast knowledge base of licensed therapists to provide users with accurate answers and guidance on building relationships.  Initially, the application featured some related articles as the user made progress. Of course, this functionality required more engagement than just recommendation. We “fed” AI with all the unique content created for that application by the professional relationship therapist. Thus, we got an AI version of the therapist that is always available for consultation with the user. It features an insightful and engaging chat under each article to ask questions, request article summaries, and receive personalized relationship advice.

The AI service model API serves as a ready-made backend component, so it considerably shortens the time to deliver required features. If we consider enterprise-level solutions, their integration approaches are more complex because they often host AI models on their hardware. Therefore, costs will increase due to additional expenses on hardware, data preparation, and development. 

As a result, the costs associated with developing and implementing AI differ depending on your project specifics. The following estimates for the integration costs are based on the integration complexity. The expenses can also vary based on the salary rates of the technical specialists involved and the actual hours spent on completing tasks. To get precise estimates for your project, it’s best to consult directly with professionals or development service providers.

Support costs

Ongoing support is essential to ensure smooth operation and troubleshooting as the customer base grows and usage patterns change. These expenses are commonly calculated within the overall expenses to maintain and update the application. They typically account for 15% of the total development expenses.

Integrating a ready-made AI soon is cost-saving because it eliminates the need for extensive research, development, and testing, reducing both time and resource investment. If we compare the received cost estimates with expenses on building custom AI-powered functionalities, AI teams spend thousands to millions of dollars monthly to collect data, train new AI models, maintain powerful hardware, introduce scalable infrastructure, and handle many more resource-consuming tasks. On your turn, you gain the full benefit of integrating the latest app functionality in no time and within a reasonable budget. 

HOW TO USE AI IN AN APP: EXAMPLES OF AI INTEGRATIONS

The widespread adoption of AI components is fostering a collaborative ecosystem where companies can build on each other’s innovations. This not only accelerates technological advancements but also reduces the time and cost associated with developing new AI solutions from scratch.

What about bringing some real-life examples of using AI in different apps? Sharing the experiences of others has always been a valuable source of inspiration and bright ideas to differentiate your products.

With a wide range of possible use cases, artificial intelligence focuses on the main goal of adding value to your application. As long as the team aligns AI initiatives with overall business strategies, the results can be impressive. There is no better thing than feature variety to guide you toward the diverse AI market. So, what AI-powered functionality has recently gained in popularity and might work for your project?  We’re going to focus attention on not only functionality but also the charged costs of using them in your app.

Intelligent search and filtering

AI enhances the search experience by making it more accurate, relevant, and user-friendly. These features are essential for applications that handle large volumes of data or content, such as eCommerce platforms, content management systems, or enterprise software. They focus on streamlining the search process with intelligent filters and suggestions.

Sampled pre-built AI solutions: Azure AI Search

AI fee example: The team can use the semantic ranker’s first 1000 requests per month for free with paid pricing plans ($73.73-$5,604.21 per month) and $1 per 1000 additional requests.

Speech recognition

This functionality has evolved significantly and now supports a wide range of applications and functionalities. It powers applications with such advanced functionality as voice-to-text conversion, voice search and commands, note-taking, speech translation, speaker identification, interactive voice response, and many more. 

Sampled pre-built AI solution: Amazon Transcribe Medical

AI fee example: Amazon charges $2.25 per 30 minutes of physician-dictated audio notes and $3.375 per 45 minutes of telemedicine conversational audio

AI-powered image recognition

Image recognition with AI has become a cornerstone of numerous applications, offering powerful capabilities for analyzing and interpreting visual data. It involves functionalities like object detection, facial recognition, image-based queries, interactive experiences, personalized content, etc.

Sampled pre-built AI solution: Google Cloud Vision API

AI fee example: The team can use the first free 1000 units a month for landmark detection, whereas units 1001 to 5,000,000 are priced at $1.5 per 1000 units, and units over 5,000,000 are $0.6.

Personalized recommendations

Artificial intelligence as a powerful tool can offer personalized suggestions based on your user behavior, preferences, and past interactions. By analyzing patterns and trends, the trained model can effectively predict and automate required actions. It learns common preferences and habits, allowing your app to tailor recommendations and responses through personalized interactions.

Sampled pre-built AI solution: Vertex AI Search

AI fee example: The first 20M predictions for media recommendations with Vertex AI Search cost $0.27 per 1000 predictions.

Chatbots

The other popular AI feature provides a wide range of functionalities, from instant customer service to automating repetitive tasks. Moreover, AI-powered chatbots learn from each interaction, improving their accuracy and efficiency over time. This continuous learning enables them to handle various queries and provide more precise responses, enhancing the overall customer experience. 

Sampled pre-built AI solution: ChatGPT API

AI fee example: The more cost-effective GPT-4o mini model set charges $0.15 for 1M input tokens and $0.6 for 1M output ones.

Predictive analytics

This AI feature is increasingly integrated into apps across various industries to enhance decision-making and operational efficiency. It can help forecast customer purchase patterns, identify marketing channels, anticipate and mitigate risks, provide data-driven insight, predict equipment failure, and more.

Sampled pre-built AI solution: Vertex AI AutoML

AI fee example: AutoML charges $3.234 per node hour for training the model on your video data.   

These are just a few examples, and the variety of functionality tends to extend at a rapid pace. Instead of costly and time-consuming custom development, businesses favor these unique opportunities to access the latest AI advancements without additional effort.

CHALLENGES OF AI INTEGRATION

Discussing how to use AI in an app won’t be complete without focusing on AI integration challenges. Of course, integrating ready-made solutions is always less challenging than AI development from scratch. 

The team approaches leveraging existing, tested models and frameworks, allowing quicker deployment and lower development costs. At the same time, it’s better to be aware of some related challenges. So the team becomes ready to navigate them and unlock the full potential of chosen pre-build solutions.

Data challenges

Although the potential data security challenges associated with AI seem overwhelming, they can be effectively managed through careful integration. This begins with selecting the appropriate AI tool. The team specifically focuses on ensuring data compatibility with the AI model and tailoring it to specific requirements and security compliance. It’s recommended to go with solutions that support 

• Sophisticated data encryption
• Network security standards
• Clear security reporting
• Centralized identity and access management

Security considerations

Without a risk-focused AI integration strategy, businesses could fail to meet essential compliance standards. Larger or medium-sized enterprises are statistically more inclined to adopt AI; thus, they need to remain more cautious and meet the demands of shareholders, clients, and regulators. The team focuses on implementing measures to ensure data privacy and integrity while adhering to regulations and standards. Make sure the chosen solutions embrace

• Legal requirements like GDPR and HIPAA
• Responsible AI principles
• Up-to-date industry regulations
• Solid ethical foundation

Integration with legacy systems

Not every existing software can be instantly powered by artificial intelligence. Built on outdated technologies, legacy systems may not seamlessly interface with modern AI solutions. Differences in data formats, communication protocols, and system architectures can create significant integration hurdles. Bridging these gaps often requires additional middleware, custom APIs, or even substantial modifications to the legacy system itself. It’s essential to embrace the flexibility and scalability needed to support chosen AI tools. It commonly involves ensuring the compatibility of required solutions and existing system infrastructure, which sometimes leads to upgrading or replacing the legacy solutions.

Resources and cost

Instead of building a model from the ground up, utilizing and fine-tuning existing pre-trained models to meet specific business needs is often a more practical and cost-effective strategy. When OpenAI spent around $4.6 million to train GPT, which was then adapted to develop ChatGPT, other teams got the opportunity to utilize the solutions presented with it. Without the need for extensive data collection or model training, they favor extensive ready-to-use features. However, training beyond supported functionality adds to the final integration and development costs, and it requires the involvement of AI experts. If you decide to extend the functionality of chosen solutions, be prepared to invest in training the model beyond its out-of-the-box capabilities.

Reliance on third-party services

The next challenge is related to the fact that relying on external platforms also comes with some risk. If their services encounter problems, everyone understands that AI capabilities within your application could be compromised. It’s vital to partner with experienced providers like OpenAI, AWS, Google Cloud, etc. They offer a proven track record in AI development and integration. In turn, your team learns from their successful case studies, testimonials, and industry-specific experience. Besides, it would be best if you chose providers with ongoing support and maintenance services. This ensures that the AI solutions remain practical and up-to-date with the latest advancements and improvements.

If the team evolves a clear integration strategy, they jump at the opportunity to set realistic objectives and find reasonable solutions. Along with prioritizing AI initiatives, the professional team knows how to handle the arising challenges. Good practices and shared experiences from others help to look at integrating AI into an app from a different perspective.

REASONS TO INTEGRATE AI INTO YOUR APP

Embracing AI ready-made solutions can be a game-changer. It offers quick and cost-effective ways to harness the power of artificial intelligence. They are designed for easy integration, allowing you to swiftly enhance your systems without starting from scratch. 

If you are still wondering why artificial intelligence is worth considering for your application, let’s give you some additional reasons. What does artificial intelligence provide to your business?

Boosted productivity

AI can streamline user workflows and provide intelligent insights, allowing the team to focus on higher-value activities and achieve more in less time. By increasing their engagement and satisfaction, AI technology adoption contributes to productivity gains.

Improved decision making

Artificial intelligence can swiftly process enormous amounts of data and provide insights for intelligent, informed app choices. AI-powered analytics enable teams to stay ahead of the competition and adapt strategies on the go.

Enhanced user experience

It gets much easier to analyze user behavior and customize experiences to match their preferences. That is precisely why teams increasingly integrate AI into mobile and web apps to enhance satisfaction and loyalty.

Cost savings

The benefit of integrating AI ready-made options also goes with optimizing resource allocation and minimizing errors. It can significantly lower operational and maintenance costs by focusing on scaling operations, automating repetitive tasks, reducing development costs, etc. 

Functionality variety

It’s an excellent opportunity to power the app’s functionality with AI, as it allows the application to perform a wide range of tasks. These include personalized recommendations, demand forecasting, speech recognition, automated replies, chatbots, and other methods to meet diverse user needs.

Market competitiveness

Integrating AI can differentiate your application from competitors. It supports providing new features and capabilities to attract and retain users. Staying ahead of the competition helps to maintain relevance in the rapidly evolving market.

Today’s market has recognized AI’s valuable benefits, and app development teams actively leverage them. Whether you want to add a virtual assistant, personalized recommendations, or intelligent search, ready-made solutions help turn advanced AI capabilities to your advantage.

CONCLUSION

The flexibility offered by these integrations transforms how businesses operate to stay competitive in a rapidly evolving market. The trend of integrating AI into apps across different domains is particularly evident for companies ready to evolve with user needs. Whether you are an established company or a startup, it’s time to define your AI use cases to remain competitive and advance your business with sophisticated products and services.

As AI continues to grow, we expect to see even more innovative applications and business models emerge, further driving the market’s growth and impact.