In November 2022, OpenAI – a San Francisco-based AI research and deployment company, changed the world’s perspective of what artificial intelligence is and how people can use it daily. The company introduced ChatGPT. To say this launch was successful is to say nothing. In just the first week after launch, ChatGPT attracted 1 million users, quickly turning into 100 million weekly users by November 2023. Naturally, this groundbreaking success led many businesses to explore how to integrate AI into an app to potentially mirror OpenAI's achievements.
I’m a Delivery Manager and PM Lead at Uptech, and I’ll be happy to guide you through the stages of integrating AI into your app.
In addition to that, we’ll explore:
- why AI integration is important;
- how to choose the right AI tech solutions for your software,;
- what insights we at Uptech drew out when working on our generative AI projects.
Why Companies Should Embrace AI Integration in Software App Development
According to McKinsey & Company's 2023 State of AI Report, 79% of participants reported using generative AI in some form, whether in their professional environment or personal activities, with 22% indicating they frequently utilize it in their work. And if these numbers don’t impress or convince you, here’s the list of the key benefits of incorporating AI in software app development for startups.
Personalized offers
Did you know that 91% of consumers are more likely to shop with brands that acknowledge them, recall their preferences, and provide personalized recommendations? That’s what Accenture found out in their Personalization Pulse Check.
Thanks to AI, you can present tailored offers and suggestions to your app users. AI personalizes user interactions, making apps more intuitive and responsive to individual preferences and behaviors. However, this will work if an app can collect and process users’ data.
Increased user engagement and retention
According to HubSpot, it's five times more expensive to acquire new customers than to retain existing ones. Moreover, a compelling argument for focusing on customer retention is that a mere 5% increase in retaining customers can boost company revenues by 25-95%.
AI personalization comes in handy here too. It leads to higher engagement rates and customer satisfaction. This, in turn, boosts conversion rates and retains users through personalized recommendations and offers.
Process optimization and automation
Of course, one of the biggest advantages is to have AI algorithms automate routine tasks: Automation may help optimize any business process, from customer service inquiries handled by chatbots to content recommendations to text summarization.
Let's take, for example, our project at Uptech called Hamlet. This tool uses the OpenAI DaVinci Summarizer to help users quickly summarize text. By integrating this AI technology, Hamlet allows for the efficient extraction of essential information from large documents, streamlining the process for users who need to digest a lot of content quickly.
In addition to speeding up operations, AI automation reduces human error and frees staff to focus on more complex tasks.
Data insights
AI excels at analyzing large datasets to uncover trends and insights that might not be visible to the human eye. These insights can inform business strategies, improve decision-making, and identify new opportunities for growth.
For nearly five decades, the scientific community grappled with understanding how proteins fold, a puzzle that remained unsolved despite various attempts and significant research. In this context, DeepMind introduced AlphaFold, an AI-driven breakthrough that not only predicted protein structures with remarkable accuracy but also showcased AI's unique ability to uncover complex insights that surpass human analytical capabilities.
Cost reduction
Using AI for product maintenance and support can really help cut down costs. It gets smarter about how to use resources better, predicts when things might break down to avoid big repair bills, and can even make energy use more efficient. Plus, AI opens up new possibilities, like being able to translate languages in real-time or recognize images and voices accurately, which can save a lot of time and money in the long run.
This isn’t an exhaustive list of all the perks that integrating AI into apps can bring. But these ones definitely deserve special attention. On this positive note, let’s move on to the guide explaining how to integrate AI into an app.
Step-by-Step Guide to Integrating AI into Your App
Before we start your journey through the guide explaining how to integrate AI into an app, we’d like to address a few things. We do understand that you, as a business owner and/or a decision-maker, won’t be doing all the technical things. However, it’s important for you to get a full picture of how the process works.
If you don’t feel there’s enough in-house capacity, you can outsource AI integration into your app to a company with expertise. They will do all the tech-related tasks for you, leaving you at the steering wheel of making business decisions.
Empower your app with our AI development services. Expert integration, your strategic decisions. Contact us for more info.
Still, it will be helpful for you to get acquainted with the core steps of how to incorporate AI into an app. There are 7 steps:
- Identify needs
- Choose the right technology and tools
- Build your team
- Collect and prepare data
- Train and fine-tune models
- Integrate and test the models
- Monitor and optimize them
Step 1. Identify needs
There are so many aspects your business can benefit from AI, it’s crazy. However, you should not create AI applications just for the sake of creating them.
Define a business problem AI will solve
The journey commonly starts with deciding the basics – you must figure out what specific business problems you want AI to solve or help you with. Then, based on what you want to achieve, you will be moving forward to choosing the right AI technology.
For instance, if your business relies on customer support, integrating an AI chatbot into these workflows will substantially reduce response times and free up your team for more complex queries. Or, if you operate in the financial sphere, you probably won’t do without AI systems capable of detecting fraudulent activities or financial advisors. The latter are developed using generative AI for banking systems. Speaking of which, we have an article dedicated to generative AI implementation, so make sure to check it out too.
It's also important to consider the costs of AI integration into an app, as developing and training sophisticated chatbots requires extensive data and might be costly for smaller operations.
Select the optimal AI integration approach
Another crucial decision to make at this point regarding AI integrations for businesses is whether you want to build your own AI model from scratch, e.g., create your own transformer model, or fine-tune one of the existing AI platforms, e.g., integrate ChatGPT via an API provided by OpenAI.
A word from us regarding this: If your company isn’t of a Google-like size and AI isn’t what your whole idea and business is built on, the second option is how things are usually done. But of course, there might be cases where developing your own model is a necessity. For example, if your operations involve confidential data (e.g., patient data in healthcare) or you have unique problems that can’t be solved with available models.
Taking this into account, the rest of the guide will be mostly focused on explaining how to create an AI application using the existing AI models and tools.
Step 2. Choose the right technology and tools
Artificial intelligence is an umbrella term for various technologies, including machine learning, deep learning, and data mining. In layman’s terms, any real-life data product built with those technologies can be called AI-based.
So, once you have defined your problems and picked the approach to solve them, the next step comes – choose the most appropriate AI technology.
Machine learning and predictive analytics
Machine learning (ML), a subset of AI, is about developing and training algorithms to analyze data and uncover whatever patterns there are in it. As ML processes more data, it adjusts and refines its algorithms based on past outcomes and provides better results.
Machine learning can be used for:
- real-time data analysis;
- trend discovery;
- customer preference prediction, and more.
Predictive analytics uses machine learning and advanced statistics approaches to forecast future events based on extensive historical and current data.
Predictive analytics can be used to:
- forecast market trends;
- customer behaviors;
- or potential risks, among other things.
Natural language processing (NLP) for enhanced interactions
Natural language processing (NLP) is also a subset of artificial intelligence. It enables machines to understand and interpret human language. For that, NLP uses a mix of linguistics, statistics, and machine learning, making it possible for machines not only to decipher text and speech but also to understand context, intent, and sentiment similarly to how humans do that.
NLP applications include:
- text classification, e.g., organizing vast unstructured data like emails or tweets;
- information extraction, e.g., pulling specific details from texts;
- machine translation and language modeling, seen in AI like GPT-4 which generates human-like text.
Image and voice recognition technologies
Image recognition (often called image classification) is the AI approach that identifies and classifies images into predefined categories. This is the power station behind software capable of recognizing and differentiating objects within images.
Image recognition applications include:
- face recognition for security purposes;
- visual product searches similar to Google Lens;
- medical imaging analysis, such as identifying anomalies in MRI scans, to name a few.
Voice recognition works similarly, but its task is to identify and interpret spoken words. This AI technique is used in applications like
- virtual assistants;
- speech-to-text services;
- and voice-activated controls.
Generative AI for creative tasks
Generative AI refers to machine learning techniques that can create new content, such as images, text, and audio, by learning from existing data.
The two main gen AI models include
- generative adversarial networks (GANs) for producing visual content;
- transformer-based models like generative pre-trained transformers (GPT) for textual generation.
Generative AI has a wide range of applications across various industries. For example, in healthcare, it can enhance the quality of medical images for early disease detection, while in marketing, it can tailor messages for specific audience segments.
So as you can see, the choice of the technology will really dictate the rest of the process.
Step 3. Build your team
Of course, you need a multidisciplinary team to integrate AI into an application. Each member brings a unique set of skills and expertise to give you that flawless fusion of AI technologies with your app. At this stage, it's essential to understand the roles that may be involved and how they contribute to the project.
Expert tip: Additional roles, such as full-stack developers, may be necessary if your application is yet to be developed.
Getting back to the AI tasks, it’s very likely that you will need the following specialist on your team:
- Data scientists are responsible for developing and training the AI models that will power your app. They analyze and interpret complex data to create algorithms capable of making predictions or decisions without being explicitly programmed to perform the task.
- Machine learning engineers bridge the gap between the theoretical data models and their practical implementation. They take the prototypes developed by data scientists and turn them into scalable, production-ready solutions.
- Data engineers design, build, and maintain the data architecture for your AI initiatives. They ensure that data flows smoothly from source to model, enabling AI systems' effective training and operation.
- Software developers play a critical role in integrating AI components into the application. They work closely with machine learning engineers to embed AI capabilities into the existing app infrastructure, ensuring that AI functions are seamlessly accessible to end-users.
A key in creating AI apps, software developers work closely with machine learning engineers, a synergy you can explore through our product development services at Uptech.
- Product (project) managers oversee the project and ensure that all pieces come together on time and within budget.
- UI/UX designers ensure that the application remains user-friendly and intuitive, even as new AI functionalities are added. They design interfaces that make complex AI features accessible and understandable to the average user, enhancing the overall user experience.
To create an app with AI doesn't necessarily mean that every role mentioned is a must for the success of your project. The specific needs of your AI integration will dictate which expertise is essential.
For instance, if you integrate AI into your app with a primary focus on enhancing user experience with AI-driven recommendations, you might prioritize data scientists and UI/UX designers. On the other hand, a project that should optimize backend processes with AI might require more input from data engineers and machine learning engineers.
Step 4. Collect and prepare data
Data collection and preparation is probably one of the most important steps when using AI to create an app. Even if you are planning to fine-tune the existing models, you must collect and preprocess enough data for the model to provide accurate results.
Data collection
Let’s start with quantity. How much data is enough? The vague answer would be – the more data you have the better. The more specific one will be – it depends on your goals as well as on the specifics and complexity of the AI technology to be employed.
Taking into account the fact that not all collected data will be useful, it’s recommended to collect and store all data possible — internal and open-source, structured and unstructured. For example, you may get data from your custom CRM system that you use to manage client engagements, EHR software in case you are in healthcare, or even simple Excel spreadsheets.
If you are short on your internal data, public datasets are available for various analytical needs, some being free and others requiring a fee. Some examples include Google's Open Images for image data and similar repositories for speech and text recognition. These resources can be found on platforms like GitHub, offering a range of options for those seeking to augment their data pools.
Data collection tools vary by industry and business model. Online businesses eyeing personalization campaigns might explore analytics tools like
- Mixpanel;
- Hotjar;
- Google Analytics;
- Web log files.
The latter also offers rich insights, tracking user interactions such as visit duration, page views, and location.
Data preparation
The next big challenge here is to ensure the quality of your datasets. Yes, it’s not only the quantity that matters. In terms of incorporating AI into applications, the rule “garbage in - garbage out” works here. You will get the respective results if you give the model poor-quality data.
Before training, data preparation must be done. Here's when data specialists, mainly data scientists, transform raw information into a structured form ideal for analysis, with a focus on enhancing data quality. It involves tasks such as standardizing formats, cleaning data, anonymizing sensitive information, and sampling to ensure efficient and accurate analysis.
Tools range from basic spreadsheets for manual editing to automated solutions like Weka and RapidMiner for more sophisticated processing. Machine Learning as a Service (MLaaS) platforms such as Google Cloud AI, Amazon Machine Learning, and Azure Machine Learning also provide advanced capabilities for data preparation tasks.
Step 5. Train and fine-tune models
Here comes the step when you – and when we say “you,” we mean the team you have – train your AI models and fine-tune them using the prepared dataset. Depending on the complexity of the task and the quality of your data, this can be a resource-intensive step, and it often requires powerful computing resources.
You train the existing model by feeding the data into it and allowing it to adjust its internal parameters based on the patterns it recognizes. The goal is for the model to learn enough from the training data to make accurate predictions or decisions when it sees new, similar data.
After the initial training, fine-tune your model to improve its accuracy and efficiency. Here, the team makes slight adjustments to the model's parameters and possibly retrains it on a subset of the data that it initially struggled with.
For example, many businesses fine-tune BERT (Bidirectional Encoder Representations from Transformers) – an NLP model developed by Google.
- One prominent use case is Google itself as BERT has been integrated into Google’s search algorithm to better understand the nuances and context of search queries.
- Yelp employs BERT for various NLP tasks, such as review sentiment analysis and classification.
- Dyvo.ai for business – the tool we developed for precise product photo replication for eCommerce – is another example. We used fine-tuning in this project, and thanks to it, we attained precise replication of the input product photos, with results being delivered within approximately 10-15 seconds. Check out the case below for more details.
When the AI is designed for custom applications, such as chatbots, virtual assistants, or code generation tools, fine-tuning also ensures the model is optimally aligned with the specific functionalities and nuances of these applications.
Step 6. Integrate and test the models
Here comes the step when you integrate AI into the app – the trained model becomes a functional part of your software. At this point, the main task is to ensure that all AI functionalities interact with your app's existing features and work in users' favor.
Integration
AI integration mainly involves setting up APIs for your app to interact with the model. In simple words, APIs are the way for different software parts to exchange data correctly. For example, to add the ChatGPT capabilities to your app, you sign up for OpenAI API access. Most integration processes also include the billing question.
In the case of OpenAI and its GPT models, such as GPT-4, the billing is often based on the usage of "tokens." A token can be roughly equated to a word or part of a word in NLP. The AI model interprets each token, making them the basic communication units between your application and the API. For instance, in GPT-4, the pricing is set at $30.00 per 1 million input tokens and $60.00 per 1 million output tokens. This means you will be charged based on the number of tokens in the data you send to the API (input) and the tokens in the data you receive from it (output).
It's essential to understand that each AI model and platform may have its own unique integration peculiarities and pricing structures. Some might charge based on the number of API calls, the computational resources used, or the complexity of the requests.
Testing
Once integrated, you test your AI-powered app across multiple scenarios to ensure it functions as expected. This includes
- unit tests to check individual components;
- integration tests to ensure different app parts work together without conflicts;
- user acceptance tests to validate that the app meets the end users' needs and expectations.
Pay special attention to the AI-specific functionalities, checking for accuracy, response times, and how well the AI handles edge cases or unexpected inputs.
Step 7. Monitor and optimize
Post-launch, your job isn't done. You need to continuously monitor your app’s AI functionalities so that everything performs well over time and adapts to new data or changing user behaviors.
Monitoring
Implement tools and processes to track the performance of your AI features in real-time. Here are a few core things you must check on a regular basis:
- the accuracy of predictions or decisions,
- the speed of processing,
- user engagement with AI-driven features, among others.
Uptech pro tip: Be on the lookout for any signs of degradation or unexpected behavior, even minor things have a significant impact.
Optimization
Another consideration in terms of your “how to make an AI app” endeavor is proper optimization. Use the insights gained from monitoring to refine and improve your AI models. This could be anything from retraining the model with new data to adjusting its parameters to even redesigning certain aspects of the model. Continual optimization helps your app remain relevant for users.
Remember, integrating AI into your app is an iterative process. You must be ready to deal with constant testing, feedback, and improvement. The good news is you shouldn’t be doing everything by yourself. There are a lot of experienced companies, like Uptech, who offer their AI development services along with further maintenance.
Uptech Examples of AI Integration in Apps
As shown partly above, we at Uptech are no strangers to using AI to create applications for various purposes.
Dyvo.ai: Generation of fancy avatars
Dyvo.ai is an AI-powered application for generating fancy AI avatars from your selfies that we developed in collaboration with Sommo – the no-code development studio. Our approach involved three key challenges: creating realistic avatars, minimizing AI-generated artifacts, and aligning with user preferences.
- AI image generation. We employed Stable Diffusion technology (a deep learning model that translates text into images) and conducted many experiments to refine our model. This included tweaking prompts, configurations, and training parameters to ensure avatars closely resembled users' original photos.
- Cost optimization. The demand for fast avatar generation presented some financial challenges due to GPU requirements. Our solution was to utilize runpod.io for hourly GPU rentals.
- Market competitiveness. As there are a lot of AI-based applications, we aimed to differentiate Dyvo.ai through user-centric design and functionality. We utilized Bubble.io for its no-code flexibility, This helped us quickly adapt and integrate features that enhanced user experience.
Dyvo.ai for business: Creation of studio-quality product photos
Dyvo.ai for business – the expansion of Dyvo.ai – helps generate studio-quality product photos with AI. This tool makes it simple to remove backgrounds and create new eye-catching ones perfectly aligned with the brand's requirements.
Challenge
One of the challenges we faced when working on this project was removing backgrounds from diverse images. We explored various models but finally went with customizing an open-source solution and optimizing it for e-commerce on our GPU network.
Solution
To replicate products with precision, we combined ControlNET, Canny pre-processing, and Image-to-Image translation, achieving near-exact copies in seconds. Addressing requests for multi-product images, we developed a method to process and combine individual items. User customization was enhanced by allowing input on style and surroundings while automating complex configurations.
Hamlet: AI-powered text summarization tool
As we said above, for Hamlet – an AI-powered tool for text summarization – we integrated the OpenAI DaVinci Summarizer tech-davinci-003 model to optimize text summarization. The core idea of this AI integration into an app revolved around adapting the artificial intelligence to work naturally within Hamlet's infrastructure, ensuring quick and accurate summaries for users.
Key efforts were about refining the model for context relevance and developing a scalable backend to handle user demand efficiently.
Among our other projects that required integration of AI into apps were Plai and one more chatbot project. Plai, a digital HR platform, was enhanced with generative AI to summarize performance reviews and generate feedback for employees automatically.
Additionally, our chatbot project leverages ChatGPT to build a question-answer system enriched with company-specific data. It essentially operates as a large NLP model capable of handling text and various tasks.
Challenges in AI Integrations into Apps
As with any other software development project, when you build an app using AI to empower it, be ready to face certain challenges. Here, we name the most common ones.
Reliance on third-party services. Using AI often means depending on external platforms, such as GPT models from OpenAI. If these services experience issues, your app's AI functionalities could be disrupted.
Prompt engineering and customization necessity. In short, prompt engineering is when you craft inputs for AI models to produce specific outputs. Effective prompt engineering is crucial. However, the whole process demands considerable time and effort to adapt the model and fine-tune these prompts as they directly affect AI performance in your application.
Data challenges. The success of AI integration heavily relies on the availability of high-quality, ample data. Insufficient or poor-quality data will leave you with no choice but to acquire more data or engage in data labeling – when hired specialists manually annotate data to train the AI model. Additional efforts might be needed to anonymize or encrypt sensitive information.
Resources and costs. Training a foundational AI model can be resource-intensive. For instance, OpenAI invested approximately $4.6 million to train GPT, which was later adapted to create ChatGPT. Rather than building a model from scratch, leveraging and fine-tuning existing pre-trained models for specific business needs is often a more viable and cost-efficient approach.
Uptech Insights: Beyond Basic Integration
At Uptech, not only do we rely on our AI integration project experiences, but we also do ongoing AI research. Along the way, we have collected valuable insights beyond mere AI integration, and we’d like to share them.
AI for sustainable growth and scalability
One of the most important things we've learned is the importance of designing AI solutions that solve current problems and are scalable for future demands. For example, in optimizing Hamlet's text summarization feature, we had to balance processing power with the quality of summaries.
This taught us that scalable AI solutions should be adaptable and capable of handling increasing loads without degradation in performance. Due to this, we recommend cloud-based AI infrastructures that can dynamically scale and ensure your application remains robust as it grows.
Advanced analytics and AI for strategic decision-making
Our experience with Dyvo.ai highlighted the power of AI-driven analytics in uncovering user behavior patterns and market trends. Predictive analytics, a key component of strategic AI use, can forecast user actions, enabling businesses to tailor their offerings for enhanced personalization.
We highly recommend you incorporate analytics into your apps to transform the information you have into actionable data insights. This, in turn, will get you to more informative business decisions and a competitive edge.
AI ethics and responsible AI use in startups
Through numerous projects involving sensitive data, especially those in the healthcare industry, we've recognized the critical importance of ethical AI use. This, first and foremost, refers to maintaining user trust and compliance with data protection regulations, such as HIPAA.
Responsible AI is the transparent use of AI technologies with fundamentals such as fairness, accountability, and privacy. In healthcare apps, for instance, we ensure AI systems are secure and compliant, advocating for clear communication with users about how their data is used and the benefits it provides.
We encourage businesses to adopt ethical AI frameworks that prioritize user welfare and data integrity.
The Future of AI in Software Development
While no one can tell for sure what will happen a year from now or even tomorrow, our insights from integrating AI into various applications suggest a promising trajectory for software development.
The future likely holds more sophisticated applications that harness AI for deeper personalization, greater autonomy, and smarter decision-making. As natural language processing and machine learning become more advanced, the emphasis will also shift towards ensuring these technologies are used ethically. More and more people in the industry raise the question of responsible use of data and ways to secure these processes.
The ever-evolving nature of tech means that those businesses that want to stay ahead should embrace and adapt to AI and its advancements.
Ready to future-proof your business and make the most out of AI? Contact us.
FAQs
How do I start integrating AI into my app?
You start the integration of AI into an app by deciding the needs: Do you want it for a chatbot to handle customer requests 24/7, for face recognition to enhance security, or for predictive analytics to minimize fraud? This will map out the entire strategy for AI integration into your app.
How long does it take to integrate AI into an app?
The time to integrate AI into an app can vary widely, from as little as a few weeks to 4-6 months to over a year. The timeframes largely depend on the AI model's complexity, the app's specific requirements, and the integration challenges encountered.
What are the benefits of integrating AI into an app?
AI-enhanced applications provide various benefits, such as better user experience, increased productivity, intelligent automation, personalized content, real-time analytics, and better decision support.
Does Uptech have experience in AI and mobile app development?
Uptech has a proven record of projects in which we helped our clients incorporate artificial intelligence into their workflows. Our experience in AI app development is demonstrated through projects like:
- Hamlet: An AI-powered text summarization tool.
- Plai: A digital HR solution enhanced with generative AI for summarizing performance reviews.
- Dyvo.ai for Business: An application for creating AI-generated product photos.