User insights + LLMs = Mind-blowing UX

We think LLMs are amazing. Because they are. That said, as with all things tech, you very much get out what you put in. Chatbots have their uses, obviously, but if your large language model is plugged directly into your user data, you’ve turned the UX dial up to 11. Welcome to the world of AI-integrated user insights…

LLMs can have limitations

Now, as amazing as they are, for the most part, large language models are purely reactive. That is, users ask a question, and your LLM responds based on what it’s trained on (read: the open internet).

But, and this is a fairly important but, what about all that delicious data you’re missing? Data that could be informing the behaviour of your LLM, and in turn, your app and user experience?

You’re already sitting on a goldmine of information, including every download, log-in, swipe, rage-tap, refresh, and purchase, if that’s how your databases are set up. And you’ve likely been collecting it since day one. So why aren’t you using it?

Here’s an example: Let’s say your LLM is a fantastically well-trained chatbot, answering support questions of all kinds, is super-speedy, and works like a dream. Can it interpret the treasure trove of user behaviour information buried in your databases? The short answer is no, not without some very specific help…

Call in the AI Agents

Without an agentic system, your LLM is only as good as its training. Which means you’re missing out on user insights that could transform the way you engage with your users. And that’s because it’s not necessarily learning anything new; it’s not seeing the bigger data picture.

What if you had a way of constantly providing your LLM with relevant data specific to each user and in context to their goal? What if your LLM was always working to improve UX using actual insights from your own users? What if users could give feedback to the AI about they prefer it speak to them or how well it did at helping? Agentic systems can provide exactly that dimension to your data.

In addition to its obvious use as a communication tool, your API can serve as a powerful tool for transforming internal data into context suitable for LLMs. By communicating with your LLM, it can retrieve information, add context, and enable your LLM to engage with users in innovative ways. Which is as powerful as it sounds.

Agents: UX but better

You want structured data, such as user demographics, purchase trends, or browsing behavior? You’ve got it! But agents can also dive deeper, analyzing the sentiment of language in support tickets or onboarding times for feature adoption. Your LLM can then use this analysis to enhance the user experience.

For example, instead of just knowing a user’s age, your system can infer that this individual is a long-term user who is older and trying new features for the first time, and may need extra assistance. As a result, your language model will proactively offer tutorials to help improve their experience. All this happens in seconds.

It’s the difference between giving someone a fish and teaching them to fish. But in this case, you’re equipping your system to provide a lifetime supply of fish, side dishes, dessert, and coffee—each tailored to individual tastes.

Personalisation on a whole new level

Sounds pretty good, right? And we didn’t even get to the really good part yet… Built right, your agents will make your app’s personalisation feel completely natural and intuitive, unlike those awkward chatbot add-ons that no one really likes. Seriously. No one.

Agentic systems can obviously work with hard facts. But as they can also make inferences from your user insights and behaviour, they’re a game-changer. Because it means that your version of personalisation can be anything you want it to be. It’s no longer just about product or service recommendations.

Of course, it is those things, but it’s also proactive troubleshooting, and addressing pain points that you didn’t even know existed. All of which results in an app that feels anticipatory, with flawless performance and delivery for each and every user.

Build it right: Design is everything

Now, we’re not saying you only get one go at getting your LLM agents right… What we are saying is that it’s super important to get it right as early as possible. Iteration is key. But your foundations are crucial.

As your API is where your data is translated into specific inputs for the LLM, the more tailored those inputs are to your app, users, or products, the more useful your LLM agent can be. Think about identifying a core use case and having your LLM agent power the user flow. This can underscore and maximise the power of your data, and in turn, your user experience.

The future is agentic

As AI develops and LLMs become more powerful and effective, users will want - and expect - apps to behave more like essential, intuitive partners than passive tools they’re operating themselves.

Those start-ups - and established companies - who invest in this tech early doors will find themselves with one helluva competitive edge. By truly harnessing the power of their data and filtering it through your agents and LLMs, you can create apps that work with and for you and your users.

And let’s be honest, there aren’t many businesses that wouldn’t benefit from agentic LLM architecture. From health and wellness to retail, finance, publishing, and everything in between, if you’ve got users, you could be reaping the benefits of your own data and transforming your user experience and company.

So drop us a message and let’s make mind-blowing user insights work for you!