MacPaw Innovation, Technology & Agility

How is your team integrating AI and ML into the product development process, and what specific improvements have you seen as a result?

When it comes to the product development process, we at MacPaw actively use a variety of AI/ML tools, such as ChatGPT, Claude, Copilot, Midjourney, DALL-E and others. This depends on the specific tasks and roles within the team. 

For example, product managers use ChatGPT and Claude to plan key product development stages and create process descriptions in Confluence. Engineers who write code proactively use Copilot to speed up development and improve quality, while QA specialists use it to write tests, which enhances the overall quality of the code. And designers use Midjourney and DALL-E to quickly generate visualizations of their concepts. While these images are not the final product, they help rapidly demonstrate ideas to the team.

The key improvements are speed and quality. Speed increases because each specialist has an AI assistant that helps with creative processes, from coding to design.

What strategies are you employing to ensure that your systems and processes keep up with the rapid advancements in AI and ML?

Much depends on the size of the organization and its ability to allocate dedicated units for such tasks. In our company, there is an AI research department that focuses on researching the latest advancements in AI even before they become widely popular. Our researchers begin studying relevant technologies as soon as academic papers appear on platforms such as arXiv or after key conferences like NeurIPS or CVPR. The main areas of research include computer-human interaction, productivity, utilities and cybersecurity.

Our researchers explore possible applications of these innovations and their integration into our products and workflows. The key strategy is having a specialized research department. For us, these are the AIR and also technological research and development center departments, which continuously work on implementing the latest technologies. This is the most effective approach if the organization can afford it.

If a dedicated research team isn’t possible, organizations should assign at least one person in the team for investigations. Otherwise, they should split time — 50 percent on core tasks, 50 percent on tech research. This helps keep up, though fully catching up with the rapid pace remains tough. 

Can you share some examples of how AI/ML has directly contributed to enhancing your product line or accelerating time to market?

Yes, we have examples of how AI solutions have significantly improved the user experience. For instance, in Setapp — our curated app subscription service for macOS and iOS, which offers a wide range of subscription-based apps — we noticed that users are not specifically searching for apps but rather looking for solutions to their problems. In other words, they need a problem-solving tool, not just an app. When the search is based solely on app names or keywords, the relevance of the results decreases. However, we integrated AI into the search process, allowing it to better understand the user’s intent. As a result, the number of relevant and successful recommendations has significantly increased, improving the overall user experience.

Another example is our malware detection technology, Moonlock Engine, which powers CleanMyMac. AI is now being used in malware detection, and we believe this will greatly enhance the product’s functionality in the future.