App Development | Apurav Gaur · September 25, 2026 · 8 min read You’ll likely not want to start over and rebuild the entire thing if you already have an app and are aiming to add AI features. The good thing is that in most cases you don’t need to. By using AI API integration, you can link your current application to an AI service and include features such as chatbots, document summaries, recommendations, or smart search. It’s not merely a matter of linking an API when introducing AI; you also have to consider how it will function with your existing system, how customer data will be managed, and what will occur if the AI provides the wrong answer. The uptake of AI is also occurring at a rapid rate. According to McKinsey’s 2025 survey, 88% of organizations use AI in at least one of their business functions, whereas 78% did so the year before.¹ Yet, only about 6% said they had experienced a meaningful profit impact. That is one thing: incorporating AI into an app. But making it useful for the business is a different matter. How AI API Integration fits into your existing application? A simple way of putting it is that ‘your app talks to an AI API’. However, in an actual application there is generally more going on in the background. A typical setup has three main parts: Our app: It’s the place where users interact with the AI feature. The backend that you currently have deals with your database, business logic, and user information. AI service layer: It links your backend system to the AI provider and handles the AI requests. The AI service layer can also handle things like: To create prompts with the appropriate information. Checking AI responses. Handling API errors and retries. Keeping an eye on usage and costs. This arrangement also means that your AI integration remains separate from the rest of your application, making it easier to update at a later stage. Example: Adding a Support Chatbot to an Ecommerce App Suppose that you already have an ecommerce application together with an order database, a returns system, and customer support. This type of work often falls under custom software development, where the new AI feature needs to work with an existing application and business logic. Now you want to add a chatbot that can answer questions like: Where does my order stand? May I return this item? What does the return policy entail? How does it works Obtain the correct information Whenever a customer enquires regarding an order, your backend system retrieves the appropriate order status, delivery details, or return information. The artificial intelligence does not need the whole of your database; it only needs the information required to answer that question. Make the request to the AI The backend forwards the customer’s question together with the pertinent information and instructions. For instance, the AI can be told to respond only on the basis of the information given; if anything is missing, it should ask the customer to get in touch with support rather than making guesses. Check the response Your application may verify that the answer refers to the correct order and complies with your business rules before displaying it. In the event of a problem, the app has the ability to display a fallback message or link the customer to a support agent. Stay in charge of important actions Imagine that a customer asks, “May I return this item?” The AI will be able to explain the return policy and verify if the order is eligible, but you will have to create the return using your current returns system. It allows the AI to be useful without granting it excessive control over significant actions. What does this mean for the business? The chatbot is able to answer ordinary questions regarding the status of orders, returns, and general support. It also provides the business with something that is measurable such as whether the amount of support work is decreasing or whether response times are getting better. The same approach can be used in other industries: Healthcare apps: The AI is able to respond to simple questions about appointments while the current system deals with booking them. Fintech apps: The AI is capable of summarising financial statements while the current system carries out the key financial decisions. The basic concept is that AI will take care of language and information, whereas your current systems will deal with the real business operations. Technical Problems You Need to Consider Although AI integration may appear to be simple from the outside there are some common problems. Excessive data included in the prompt You mustn’t send the whole customer database to the AI each time you make a request; instead, your backend system should choose the data that is actually relevant. The responses from AI can differ The same question doesn’t always elicit the same response from AI. Tests should therefore verify accuracy, the structure of the response, and the fact that the response conforms to your rules. The app may become slower because of AI AI requests may take longer than ordinary database requests, and the use of loading states and streaming responses can make the experience seem smoother. The limits and costs of the API The more it is used, the more important API limits and token charges become, and keeping an eye on things along with handling errors properly can help avoid unexpected expenses. Prompt injection The documents uploaded by users might include instructions intended to manipulate the AI, and it is necessary to keep reliable instructions separate from user content so that the AI’s output does not directly cause sensitive actions. Security and Data Handling This is the point at which an AI prototype must develop into a proper production feature. You should keep API keys on the server; they must never be included in the frontend and should be stored securely, not being logged in plain text or shared between environments without appropriate controls. Make sure that you know the data that you’re sending. Before any customer information is included in an AI prompt, verify precisely which fields are being sent and whether or not they actually need to be sent. For example, unless the support chatbot needs the full account number, don’t send it. Try to use as little personal data as possible and, where you can, remove or cover up the PII before sending the information to the AI service. It’s important to keep audit logs. When an AI feature is involved in decisions that are presented to customers, it will be useful to record the information that was sent, the response that the AI returned, and what the application then did with that response. Most important of all, have a fallback plan when the AI is wrong. In cases involving sensitive matters such as refunds, financial decisions, or medical information, the AI should not act as the last person to make a decision unless there are appropriate checks or a human review. What factors can cause an AI API Integration project to slow down? It isn’t always the AI that poses the difficulty, for your current application can as well present challenges. Some common examples are: There is an outdated backend that is hard to connect to external APIs. Data is distributed among various systems. Further testing is required in order to enhance AI responses. Unexpected API costs. There is no monitoring carried out after the launch. That is the reason why it is better to specify the feature and the requirements before development begins. Is your current application ready for AI? Before starting, ask yourself: What specific problem do I really want artificial intelligence to solve? Does the AI need access to real customer data? May I begin by mentioning one minor feature? What would occur if the AI provided the wrong answer? Is the backend I’m currently using ready for an external API? It’s not necessary to create a large AI system right from the beginning, since beginning with a single well-defined feature makes it simpler to test, measure, and improve. Where to Go From Here At Deorwine it’s possible to connect new AI capabilities with the apps and systems that your business currently uses. The appropriate method to adopt will vary according to the technology you currently have, the feature you wish to add, and the kind of data in question. If you wish to include an AI chatbot, smart search, document processing, or some other AI feature, the first thing you should do is determine what your current app really needs. Talk to Deorwine about your app FAQs Shall I have to rebuild my app in order to incorporate AI? On the contrary, in most cases AI can be incorporated as a new layer which connects with your current app, backend, data, and workflows. Shall I have to train my own artificial intelligence model? By no means can you use an AI service that is already available from companies like OpenAI, Google, or Anthropic; custom model training is generally only required in specific cases. What would occur if the AI provided the wrong answer? The integration should include checks and alternative options, and the AI must not on its own approve refunds or take sensitive decisions. Is it safe to transmit customers’ data to an AI service? It could be, but the data has to be dealt with carefully. You should only provide the AI with the information that it actually needs and make sure you understand how the chosen provider handles that data. How long does the process of integrating AI take? A simple feature can usually be developed in just a few weeks, while more complicated integrations will take longer or not be possible depending on your present app, data, and requirements. How can I tell whether my app is ready for AI? Begin by examining the backend, the data, and the problem that you want to have the AI address. A brief technical conversation generally helps to determine what changes are needed prior to starting development. Still weighing it up? A short conversation is usually enough to get real clarity. Get in touch with Deorwine → Share Facebook Twitter LinkedIn The Author Apurav Gaur Co-founder, Deorwine Infotech