Intelligent Interfaces: Why LLM-Controlled UI Will Replace Traditional Dashboards

Some thoughts and process documentation of the trials and tribulations behind how Upsolve AI (YC W24) arrived at our LLM-controlled UI solution, presented in our latest Y Combinator demo.

The Evolution Gap

Software has undergone remarkable transformation over the past two decades—from clunky desktop applications to fluid cloud services, from rigid interfaces to adaptive experiences. Yet dashboards remain stubbornly static. They look virtually identical to their counterparts from 15 years ago. Rows of charts. Dropdown filters. Static legends. The same rigid architecture persists while everything around it evolves.

The contrast is stark between modern interfaces and traditional dashboards. On one side: flexible, intuitive systems that adapt to users or designed to be familiar (such as conversational interfaces). On the other: dashboards quite literally stuck in space and time, requiring users to adapt to them. It's about time to reimagine how data and dashboards can work for the end user—not the other way around.

This is exactly what our team at Upsolve AI (YC W24) have been working on. Here we document and showcase the various iterations we went through to get to the LLM-generated UI that creates a role-based, hyper-personalized view for each end user.

If you want to skip the behind-the-scenes trial and tribulations and skip to the end result, watch our Y Combinator demo that got featured not once but TWICE. Because it's just. that. good.

The Challenge

The core problem becomes apparent when you examine complex enterprise dashboards. When a dashboard contains multiple charts pulling from different database tables—each with their own structure—traditional filtering becomes exponentially more complex.

Our journey through four generations of filtering solutions revealed a clear trajectory:

Version 1: Manual Configuration

Our initial approach required explicitly mapping filters to specific columns in each chart. The result was predictably problematic:

• Configuration became tedious and error-prone

• Schema changes broke filters

• Traceability was non-existent

Version 2: Structured Relationships

We created chart-level filters linked to dashboard filters, which improved traceability but:

• Multiplied the configuration burden. The million clicks became 3 million clicks; users had to configure many new filters everywhere

• Failed to accommodate new charts seamlessly

Version 3: Semi-Automation

Automating the creation of chart-level filters brought us closer to an ideal state:

• Configuration became manageable

• Listeners maintained integrity as data evolved. Everything was kept up to date as the data & dashboard changed through listeners

Version 4: The Intelligence Breakthrough

LLM-controlled filtering changed everything:

• A single natural language prompt could manage the entire filtering system

• The LLM correctly interpreted context and applied filters appropriately

• Users could express intent without understanding underlying complexities

LLM-controlled UI as The Path Forward

At Upsolve AI (YC W24) AI, we're now considering what might seem radical to many: moving entirely to LLM-controlled interfaces and eliminating traditional UI controls altogether. Is this crazy? Perhaps. Has anyone else attempted to completely replace their UI with an LLM-powered interface that converts natural language prompts directly into the visualization the user needs? Few have ventured this far.

Filters have been such a fundamental, long-standing concept in analytics and dashboarding that fully retiring them feels almost heretical. But what seems unthinkable today becomes obvious tomorrow.

While we currently maintain a hybrid approach, our testing shows the future clearly belongs to intelligent interfaces.

The question isn't whether LLM-controlled interfaces will replace traditional dashboard design, but the value that lies within this kind of shape-shifting UX. The organizations that embrace this shift early will gain significant competitive advantages in how widely, quickly, and effectively they can extract value from data.

If you're ready to Upsolve your product and see the power of customizability and hyper-personalization for yourself, let's chat!