Steel ai

A 70-year-old leader in the steel industry (referred to as "Steel.ai" under NDA) had hit a wall in its AI adoption. Its core AI software, "Calculating," was meant to improve materials estimation, but it wasn't being adopted internally. At the same time, the organization had no unified system for training its team, tracking platform usage, or managing its innovation initiatives.

Branding

E-commerce

Graphic design

Selected project from my time at Acueducto
Role

Product design

Client

Steel.ai

Date

August 2025

The problem

The organization was mature enough to scale its AI capabilities, but it ran into a critical obstacle. Its core software, "Calculating" (an alias, for confidentiality), was meant to improve materials estimation, yet it wasn't gaining internal adoption. When users tried to audit the algorithm's calculations, the interface returned massive, unstructured blocks of code — making the numbers impossible to validate and eroding trust in the system. In parallel, the company had no integrated system for training its team, monitoring app usage, or centralizing its innovation initiatives.

The challenge

The challenge came in two parts, across different phases of the project. First, a tight one-week sprint to fully redesign the "Calculating" tool, with the goal of closing the trust gap between the engineers and the AI.

In the second phase, the challenge was structuring a scalable, dual-purpose Central Hub: a place to centralize the company's AI projects and the foundation for a design system across their digital products.

In the second phase, the challenge was structuring a scalable, dual-purpose Central Hub: a place to centralize the company's AI projects and the foundation for a design system across their digital products.

The solution

To meet the one-week deadline for "Calculating," I spent a full day interviewing a specialist engineer to understand exactly how they evaluate an estimate. I designed a new interface that displays the data the way their legacy software does, so they can check steel quantities against a familiar format and correct errors in the tables on the spot through an AI text prompt.

I tested the prototype with the engineering team; they were able to validate estimates without leaving the interface, turning a frustrating bottleneck into a fast, reliable workflow.

In a later phase, I took on the broader project ecosystem. For the Central Hub, I split the interface by user role: Employees can access training and submit proposals to bring AI into workflows they think could be optimized, while Admins can build multimedia courses, monitor usage stats across the three main products, and review those proposals.

The Central Hub is now in active use by hundreds of employees building their digital skills, and gives leadership real-time operational metrics to inform their decisions.

In a later phase, I took on the broader project ecosystem. For the Central Hub, I split the interface by user role: Employees can access training and submit proposals to bring AI into workflows they think could be optimized, while Admins can build multimedia courses, monitor usage stats across the three main products, and review those proposals.

The Central Hub is now in active use by hundreds of employees building their digital skills, and gives leadership real-time operational metrics to inform their decisions.

To keep implementation consistent across these constantly evolving web platforms, I defined the tokens and components, which the engineering team implemented in shadcn/ui.