Background

Daily Racing Form is a legacy horse racing data platform used by bettors, handicappers, and racing professionals to evaluate races through statistics, historical performance, and expert analysis.
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The strength of DRF has always been its depth of data. However, that same data density created a major usability challenge. Professional users could interpret the information, but newer or casual users often struggled to understand racing terminology, compare horses, and make confident decisions.

DRF AI was created to bridge this gap by transforming dense racing data into a conversational experience where users could ask questions and receive clear, contextual insights.

Design Challenge

How might we help users understand complex horse racing data faster, while creating a scalable design foundation that could unify DRF’s broader product ecosystem?

Solution

Global Design System Foundation

Before designing the interface, I redefined the DRF Global Design System to create a single source of truth across products. This included standardizing typography, colors, spacing, components, and documentation so that all the DRF products I was working on could feel native to the ecosystem.

DRF AI Product Experience

After attending the "Computational Neuroscience" event at kite UHN, by Dr. Brokoslaw Laschowski, I discovered the potentials at the intersection of  machine learning and neuroscience

"In our research lab, we develop new computational and machine learning models to decode neural signals from the brain which encode information about procesies such as cognition and motor intent."

Stakeholders

Secondary Research and information maps

Key findings

Solution

Pave the path of communication and bonding

Pave is the communication bond between human and animals using Neurotechnology, through an app and a physical wearable for dogs and cats.
By leveraging non-invasive EEG technology, this system scans animal's brain and displays the translated brainwaves data in the app for the owner to monitor their pets' emotional and cognitive states. Using Neurostimulation capabilities (tDCS) users can send training cues, direct monitored communication cues, or therapeutic signals back to the pet’s brain as well.

Design process

Phase 1: Ideation to Low-fidelity

The process started with +20 early sketches and ideations on how the wearable could look like.
Then by choosing two raw ideas, I started creating low-fidelity prototypes with magnets and papers and tested on small dog sculpture.
Later I started generating refined versions of the drawings using AI (MidJourney and Dali) to have an easier decision making and visualization of the concepts.

Meanwhile, I also started making flow maps of the app, including sign up to create a user journey while collecting the determined user data.

Phase 2: Mid-fidelity

The physical prototype continued after 3D printing a real-life dog and a cat model, while researching the exact measurements and materials.

With taking inspiration from products like VR headsets and dog goggles, I came up with the idea of a cap with attachable parts that are stretchy to fit the animal's skull.

The parts include a piece surrounding the neck, a piece that covers the top of the head, and a piece between the ears that connects the top piece to the neck piece. This was later simplified into two pieces as I decided to create two sizes of the product.

Phase 3: High-fidelity

neurocap™ (wearable)

The final wearable called Neurocap™ includes two sizes of small (cats and smaller dog breeds) and large. It's made out of polyester fabric on the sides, soft woven elastic and 3D printed PLA buckles for resizing and adjusting the helmet. There's stretchy cotton used for the top piece to adjust and breathable for the animal skin, and includes 4 aluminium grommets to attach the sensors to.

The sensors have two parts which includes a top piece that is printed in PLA Silk+ and TPU on the tip (electrode) to easily click into the grommets.

Pave mobile app

Pave mobile app is a companion tool that visualizes the emotional and health data of the user's pet in real time using brainwave and muscle activity analysis. Paired with the wearable device, it offers live insights, behaviour trends, training feedback, and communication cues.