If you're not familiar with the term generative AI, you're not alone. After all, the term only existed quite recently. But the technology is already disrupting industries at a pace that many companies struggle to keep up with. Be it retail, healthcare, or finance, organizations seek ways to integrate generative AI into their respective workflow.
According to Statista, the global generative AI market will be worth $206 billion by 2030. And that definitely looks promising for founders and small business owners. Somehow, there is a growing demand to accelerate AI developments and integrate such capabilities to improve business outcomes.
Yet, many challenges remain before organizations can scale AI adoption. Questions like data security, bias, and other ethical concerns need to be answered. Otherwise, AI integration might do more harm than good in mission-critical applications.
I’m Andrii Bas, Uptech’s founder and also a generative AI expert and I’m Oleh Komenchuk, Machine Learning Engineer at Uptech. We’re one of the leaders in AI development. Since ChatGPT emerged, our team has spent time and resources exploring generative AI. We understood the complexities of integrating the technology and applied our skills to building AI-powered apps like Dyvo.ai, Hamlet, and Angler AI.
That’s why we created this how-to integrate generative AI step-by-step guide. As you read on, you’ll learn how we integrate AI into apps at Uptech, including the steps our AI developers took from start to finish. Let’s begin.
Why integrate Generative AI into your app?
We know that generative AI chatbots like ChatGPT, Gemini, and Llama can converse as humans do. The question is — how can the underlying technology benefit startups and their customers when integrated with an app?
To answer that, it’s vital to understand that generative AI is more than an intelligent chatbot.
Generative AI can:
- understand complex patterns
- analyze large volumes of data
- support decision-making
- predict possible outcomes
- create unique content
More importantly, generative AI can quickly perform such tasks beyond human limits. This opens up new possibilities for startups, which we share below.
Personalize customer experience
Customers want to be engaged differently when interacting with the app, and generative AI makes it more effortless. AI can collect and analyze user interactions and various touch points and create an app experience tailored to specific users. For example, you can integrate an AI shopping assistant that furnishes them with product details based on their past interactions.
Automate mundane tasks
We already see large tech solutions offering alternatives to get work done more effectively with generative AI. For example, Google Docs recently integrated Gemini, which allows users to create text in seconds without leaving the editor. Imagine doing the same with your apps. Instead of spending precious time on repetitive tasks, users can automate them with generative AI.
Enable new features
Generative AI enables innovation that was previously impossible on apps. Let’s say you want to create a professional headshot for LinkedIn. You would need to manually edit your photo in an editing app a couple of years ago.
Today, you can do it easily with Dyvo.AI, an app we built to demonstrate the possibilities of generative AI.
Improve security measures
Generative AI can help strengthen security posture in applications that store and send sensitive data. Deep learning models can detect anomalies and alert supervising personnel for mitigative measures. For example, mobile banking apps can integrate generative AI to detect and block suspicious transactions.
Learn how to build Generative AI solution from scratch
How to Integrate Generative AI: 12 Key Steps
Integrating generative AI into your apps isn’t just about enabling ChatGPT-like functionality. You need to consider the technology's business, operational, and ethical implications.
These are our strategies for integrating generative AI seamlessly, securely, and impactfully.
Step 1: Define objectives and use cases
Before you integrate an app with GenAI, determine if there is such a need. That’s because not every app will benefit from generative AI models. Without a clear strategic plan, you might end up investing in a solution that serves no purpose despite being equipped with the latest AI model.
So, identify areas where generative AI can be impactful. For example, you can leverage AI for content creation, product design, and business process automation. Rather than replacing humans, generative AI excels in assisting humans in creative, analytical, or laborious tasks.
At Uptech, we conduct a thorough discovery phase to identify and assess the impact of AI integration in your app. We ensure that the target user is responsive to such changes before pursuing the idea further. This way, we can help founders avoid unnecessary risks when developing their AI-powered products.
Step 2: Conduct a thorough audit
Next, you need to assess whether your startup is ready to build, deploy, market, and support an AI-infused solution. Integrating AI requires skillsets in curating datasets, training a model, and evaluating it. You’ll also need to provide computing infrastructure to host the model, application, and data, whether internally or on a public cloud.
Evaluating your startup’s AI readiness allows you to identify the risks and challenges that await should you pursue the project. Let’s say your audit shows that your team is strong in web development but not familiar with Python AI libraries like Keras, PyTorch, and TensorFlow. You’ll then need to find ways to overcome the skill gap.
This is also where you dive deeper into the areas where AI integration can be most impactful. Ideally, it’s best to support the proposal with projections and anticipations. For example, before developing an AI virtual assistant to handle customer inquiries, find out if users would find it helpful.
Step 3: Form a dedicated AI task force
The success of generative AI implementation hinges on the team that supports it. As a founder, it’s extremely difficult to take on the project alone. Rather, assemble a cross-functional team to provide different views on the project. For example, an AI developer can advise on the tech stack required to build the solution, while a product manager helps identify opportunities where AI is applicable.
Besides ensuring product-market alignment, the task force is also responsible for creating compliant, secure, and ethical AI applications. In fact, this is the most significant hurdle that prevents many organizations from adopting AI at scale. By doing their due diligence, the task force can set an ethical framework that becomes the foundation of subsequent development works.
Step 4: Choose the right AI tools
One of the key steps for integrating generative AI is to select the appropriate AI tools required. Depending on the type of AI application you want to build, the type of AI tools or tech stacks differ.
For example, if you plan to create AI apps for image generation, a variational autoencoder or generative adversarial network is more suitable than text-based models. Meanwhile, it’s better to use large language models like GPT and Llama for natural language understanding and text generation.
When choosing AI tools, also consider the ease of integrating, maintaining, and scaling them as your app usage grows. That’s why we recommend using OpenAI’s API because it’s robust, flexible, and extensive. By connecting to OpenAI with a secret API key, you can access powerful models and perform functions like:
- text generation
- Image generation
- text-to-speech conversion
- text embeddings, and more from your app.
Step 5: Establish guidelines and governance
Chances are, you’ve heard of generative AI-powered apps that demonstrate bias and make inaccurate decisions. As advanced as they are, deep learning models are still evolving and are not perfect. It’s up to us to apply guardrails and protect users from risks associated with AI, including data security, privacy, and ethical concerns.
Create a framework that promotes trustworthy and responsible AI, which will help your developers create solutions aligned with societal values.
At Uptech, we take data privacy and ethical AI seriously. Besides ensuring the model’s performance, we secure data used in training and operating the AI model with encryption and other security measures. We also practice secure coding to safeguard AI apps against OWASP top vulnerabilities.
Step 6: Develop or acquire training data
You cannot repurpose a generative AI model for a business use case without training or fine-tuning it with domain-specific data. For example, ChatGPT can answer questions about historical people but cannot provide the specifications of your company’s products. To do that, the model must learn from a training dataset.
You can create a training dataset by curating relevant data from various sources. For example, you can consolidate data from emails, feedback, and CRM logs to train an AI model to optimize sales conversion. There are several considerations to be mindful of to ensure that the training data is of high quality.
- Data volume
- Data accuracy
- Data relevance
Remember that the quality of the dataset will directly affect the model’s performance. If you train the model with data that doesn’t fairly represent the user, your AI model will produce biased results. Often, you’ll also need to clean the dataset to remove anomalies that might affect the training outcome.
Step 7: Train Gen AI models
To train a generative AI model, you need to feed it with your curated dataset. The process is compute-intense and consumes a significant amount of time. The more parameters the AI model has, the longer it takes to achieve reasonable accuracy. On top of that, data scientists must further refine the model to suit your app’s purpose.
Time, unfortunately, is something that startups don’t have in abundance. At Uptech, we recommend startups leverage pre-trained models or generative AI services from providers like OpenAPI via API services. With this approach, you don’t have to spend months getting an AI model up to pace. All you need to do is to fine-tune a foundational model.
Fine-tuning is easier and faster. It involves exposing a small part of the AI model to information specific to an industry while retaining its existing knowledge. Imagine you want to integrate an AI chatbot into a healthcare app. Instead of training the model from scratch, you can fine-tune a GPT model with one or few-shot learning.
Step 8: Prepare your application
While training the model, ensure your app is ready to be integrated. If your app is built on a legacy framework, you need to rebuild a part or the entire codebase. Otherwise, explore possible ways to integrate with the AI model without major reworks. Even if the app only requires minimum modification, list down the affected modules and possible impact on users.
For example, you can use API calls to connect with large language models if you’ve built your app for future expansion. Instead of rewriting the entire app, you modify app functions that send and receive data from the AI model.
Preparation for AI integration isn’t limited to user-facing apps or web platforms. Developers also need to ensure the backend services function cohesively with the AI model. This involves ensuring data integrity, security, and compatibility when the app exchanges data with the AI model.
Step 9: Integrate the model with the app
So, how to integrate generative AI into your software? Before you start the process, do this:
- perform the compatibility check
- conduct security check
- revise the databases, servers, and other infrastructure involved.
Your AI task force is responsible for ensuring a smooth transition and taking compliance, privacy, and the operational impact of the integration into consideration.
When working on Hamlet, an AI text summarizer, we carefully integrated the app’s user interface, backend services, and language model. We knew that meticulously considering all aspects is important to deliver an app that engages the users throughout their journey. Our effort paid off, resulting in a user-friendly app powered by an advanced language model.
Another crucial reminder is to involve app users if you plan to integrate the app they’re using with AI. Inform them before making such changes. This way, users have ample time to prepare and anticipate updates in the app workflow.
Let’s say you plan to introduce a generative AI recommendation system in an eCommerce app. You can prepare guides to help them navigate the affected features before and after integration.
Step 10: Test the integration
Whether you’re building or integrating generative AI with an app, remember about software testing. As it is still an evolving technology, generative AI may produce inaccurate results. Without appropriate safeguards, your app might breach ethical and fair principles, which negatively impact users.
Ideally, the app must pass several tests before being deployed, including
- Unit testing
- Performance testing
- Integration testing
- System testing
Your AI app should perform consistently under various conditions and demonstrate reasonably high accuracy. That’s because applications in some highly-industry can’t tolerate an app that strays beyond its specification.
For example, integrating generative AI in banking is highly challenging because of the implications a biased model can produce. Therefore, incorporating continuous testing throughout development helps in identifying and fixing problems early on.
Step 11: Optimize and iterate
Once you’re confident that the app is free from serious flaws, launch it to the users. Be prepared for any events that may arise as the AI-powered features are tested in real life. Put your support team on standby and respond to support requests promptly.
More importantly, collect feedback from users and use it to improve your app. In some use cases, the generative AI model can fine-tune itself in real time, allowing the model to improve its response accuracy immediately. On top of that, the AI model can also collect, curate, and send key data to the backend for further analysis.
Step 12: Monitor and maintain
Be watchful for bugs, vulnerabilities, and other issues that might arise after your AI app is operational. Some problems may remain latent during the testing phases and manifest themselves only under specific conditions. Respond to these unexpected circumstances quickly to keep the users satisfied.
Also, observe and anticipate evolving trends and changing user behaviors. If needed, revise the app or add new features to remain aligned with market needs. For example, a conversational chatbot for answering product questions might lose appeal as users seek a more intelligent AI assistant to help them purchase and track their items.
How Uptech can help you integrate generative AI
Uptech has successfully delivered 25+ AI-powered solutions for the past 3 years. Since LLM became publicly available, our team of software experts has been innovating solutions in computer vision, conversational AI, text generation, and more. We help global startups and SMBs integrate their apps with AI to automate, streamline, and personalize user experience.
For example, we integrated AI with Aboard AI, an app that analyzes real-time flight data to produce valuable insights. Another AI-powered app we recently worked on is Presidio Investor. The app applies an AI agent to interact with a financial database. It can analyze and retrieve information from financial portfolios, documents, and agreements.
Our AI development approach is based on a lean strategy. We made an effort to conduct product discovery to ensure your AI-powered app is targeted to the right audience. Besides leveraging our deep AI/ML expertise, you can also be assured of our secure, compliant, and ethical principles when developing with AI.
If you need a consultancy on generative AI integration or have a project in mind and want to collaborate, drop us a line. We’ll be more than happy to help.
FAQ
What is generative AI?
Generative AI is an advanced machine learning technology capable of producing unique and realistic content from the knowledge it has learned. For example, generative AI can create text, visuals, music, and video similar to those that humans make.
How do you implement generative AI?
To integrate generative AI you need to follow these steps.
Step 1. Identify the business needs for AI
Step 2. Choose a deep learning model
Step 3. Train or fine-tune the model
Step 4: Integrate the model with the app
You will also need to continuously assess AI performance and optimize the model as and when necessary. You can accelerate the effort by using readily trained models with methods like generative AI API integration.
How much does it cost to integrate generative AI?
A basic integration of a pre-built generative AI model can cost from $10,000 to $50,000. Integrating a custom generative AI solution could range from $50,000 to $500,000 or more, depending on the complexity.
Overall, the average AI cost can range from $10,000 for a simple solution or reach $200,000 and more for the complex tech part alone. So AI can be affordable. However, there are a lot of factors that impact the pricing tag of AI development.
What are the strategies for integrating generative AI?
These are strategies to make generative AI integration more effortless, secure, and trustworthy.
- Align AI implementation with business goals.
- Prioritize data security and privacy.
- Curate high-quality training samples.
- Evaluate and test AI models for inaccuracy, inconsistency, and bias.
- Monitor and adapt AI-powered apps to evolving trends and user behavior.