AI development

AI isn’t just about futuristic tech - it’s already helping businesses improve their apps in practical ways. Here’s how AI can make a real impact:

• Personalised user experiences – AI learns from user behaviour to recommend content, suggest products, and even tailor app interfaces.
• Better decision-making – AI can process large amounts of data quickly, spotting trends and providing insights to help businesses make smarter choices.
• Smarter customer Support – AI-powered chatbots and virtual assistants handle routine queries, reducing wait times and improving customer satisfaction.
• Increased security – AI detects suspicious activity and potential fraud faster than traditional methods, helping protect user data.

There isn’t just one type of AI - different technologies serve different purposes, depending on what you want to achieve. Here are a few of the most common AI applications in apps:

• Machine learning (ML): It helps apps learn from user interactions and improve over time.
• Natural language processing (NLP): It allows your app to understand and respond to human language (e.g., chatbots, voice assistants).
• Computer vision: It powers image and video recognition features, such as facial recognition or augmented reality.
• Predictive analytics: It uses historical data to anticipate user needs and suggest relevant actions.
• Generative AI: It creates content, such as text, images, or even code, based on learned patterns.

AI is already shaping the way apps are built and used. Developers are leveraging AI to improve performance, security, and better user experiences in ways you might not even realise. Some of the most common AI-powered features include:

• Code assistance: AI tools help developers write, debug, and optimise code more efficiently.
• Search enhancement: AI improves search accuracy by understanding the intent behind user queries rather than relying solely on keywords.
• Fraud detection: AI analyses patterns and detects suspicious activity in financial and e-commerce apps to prevent fraud.
• Personalised marketing: AI helps deliver highly targeted ads, promotions, and recommendations to users based on their behaviour.
• Content moderation: AI can analyse user-generated content to ensure posts are appropriate and reduce the need for manual moderation.

1. Artificial Intelligence (AI)

A broad term referring to machines or software that can simulate human intelligence, including learning, reasoning, and problem-solving.

2. Machine Learning (ML)

A subset of AI where systems learn patterns from data and improve their performance over time without being explicitly programmed.

3. Deep Learning

An advanced type of machine learning that uses artificial neural networks to process vast amounts of data, commonly used in image recognition, speech processing, and recommendation systems.

4. Neural Networks

A series of algorithms modelled after the human brain that help AI recognise patterns and make decisions.

5. Natural Language Processing (NLP)

A field of AI focused on enabling computers to understand, interpret, and generate human language, used in chatbots, voice assistants, and sentiment analysis.

6. Generative AI

AI that creates new content, such as text, images, videos, or music, based on patterns it has learned. Examples include ChatGPT and DALL·E.

7. Large Language Model (LLM)

A powerful AI model trained on vast amounts of text to generate human-like responses in conversations, automate content generation, and more.

8. Computer Vision

A field of AI that enables machines to interpret and analyse visual data, such as images and videos, is commonly used in facial recognition, object detection, and augmented reality.

9. AI Chatbot

A virtual assistant that uses AI to engage in human-like conversations via text or voice, commonly used for customer support and in-app assistance.

10. Predictive Analytics

AI and data analysis are often used to predict future trends or behaviours in recommendation engines, fraud detection, and business forecasting.

AI can bring huge benefits, but it isn’t always a plug-and-play solution. Businesses often run into a few key challenges:

For one, AI requires a lot of data to be effective. If your app doesn’t collect enough quality data, the AI features may not work as expected. There’s also the issue of scalability - as your app grows, AI models need to be maintained and optimised to keep up with increasing demands.

Security is another concern. AI typically needs access to user data, which means you need to ensure compliance with data protection regulations.

Lastly, there’s the question of cost and expertise. AI development can be expensive, and finding the right talent to implement it properly is a challenge many businesses face.

Engagement is key to any successful app, and AI can help keep users interested and coming back. Here are a few ways AI makes apps more engaging:

1. Personalised content – AI recommends articles, videos, and products based on user behaviour, making the experience more relevant.
2. Chatbots & virtual assistants – AI-powered chatbots provide instant customer support, answering common queries 24/7.
3. Predictive features – AI anticipates user needs and suggests actions before they even search for them.

At first glance, adding AI to an app might seem expensive, but in the long run, it can lead to significant cost savings and an increase in revenue. AI reduces the need for manual processes, improves operations, and enhances your user engagement, all of which contribute to better financial outcomes. Businesses that use AI often see improved customer retention and higher conversion rates, which ultimately impact their bottom line.

Security is a critical factor when integrating AI into an app. AI systems rely on large datasets, often containing sensitive user information, making data protection essential. Ensuring secure data storage, encryption, and compliance with regulations like GDPR or CCPA helps safeguard user privacy.

Another major concern is bias in AI algorithms. Since AI models learn from existing data, they can unintentionally adopt and perpetuate biases. Regular audits and testing are necessary to maintain fairness and prevent unintended discrimination.

Cybersecurity threats are also a risk, as AI-driven applications can be vulnerable to hacking attempts. Developers must continuously monitor AI models for vulnerabilities and implement strong security measures to protect against potential exploits.

Most public AI services train their models on continued usage, using the data they are provided to further improve their responses. Running your own AI service can ensure your data stays 1st party, but can come with increased complexities and costs.

If you’re considering adding AI to your app, here’s a simple roadmap to get started:

1. Define your objectives: Identify how AI can add value to your app.
2. Assess your data: Ensure you have the right data for AI-powered insights.
3. Evaluate the risks: Qualify the security and privacy considerations of the approach.
4. Understand the costs: Understand both the upfront and ongoing running costs.
5. Choose AI technologies: Select AI tools that align with your goals.
6. Work with experts: Partner with AI experts (like us!) to guide the integration process.
7. Test and optimise: Start with a small AI feature and expand based on performance.

1. What is a token?

A unit of text (word or subword) is used in natural language processing (NLP). AI models process text as a sequence of tokens rather than entire words or sentences.

2. What is prompt engineering?

The process of crafting input prompts to optimise AI responses is essential for working with large language models (LLMs) to get the best output.

3. What is fine-tuning?

Adjusting a pre-trained AI model using a custom dataset to improve its performance for a specific use case.

4. What is embedding?

A numerical representation of data (words, images, etc.) in a multi-dimensional space, allows AI models to compare and understand relationships between them.

5. What is a vector?

A mathematical representation of data points, often used in embeddings. AI uses vectors to process relationships between words, images, or other structured data efficiently.