Deloitte Hackathon app

2nd place winner

This project was made during the Deloitte Spark Hackathon 2026. The brief from Deloitte and WWF was to combine open data with the tech of tomorrow, to create a digital product that makes the planet's vital signs visible and truly actionable.

During the weekend our team came up with a solution: Foodprint.

Foodprint, utilizes three different datasets: Open Food Facts, Agribalyse and WWF's köttguiden. Using elements like gamification, nudging and social accountability it gives the user an opportunity to understand and make an actual change in their food habits.

Tools and techniques

Brainstorming, crazy 8, pain points, Figma and cursor AI.

Duration

14th - 15th March 2026

Linnéa Fältskog.pdf

Ideation

We started of by mapping out the brief. Writing down the problem, user, why it matters etc. Then we research different areas of the problem like; food, wildlife, freshwater, forests and climate. The team discussed the information each had gathered on the different problem areas and decided on food.

Pain points

The next step was to write down different pain points to the problem area. This was done together in a brainstorming session.

Crazy 8

When the pain point were gathered we started the idea generation by utilizing the crazy 8 - method. The post its were then gathered and sorted in different categories.

Visual impact:

- Graphic element, like a tree growing

- Each member gets a bigger land area with each positive action

Social element:

- See how much difference your team has made

- Show how much CO2 all users of the app has saved together

- Challenges to do together with your friends

Educational

- Show alternatives to a product with a lower CO2 footprint

- Explain why the challenge is important

Decided on the frames of the app

After completing the Crazy 8s exercise, we had a clearer idea of the app’s structure and key features. We moved to the whiteboard to map out initial frames and explore different directions.

We developed two main concepts: one focused on habit-building using geolocation, and another centered around food choices and challenges. Considering the limited timeframe of the project, we chose to move forward with the latter.

We created a flowchart to map the user journey and define core interactions.

Translating to Figma

Once the flows were defined, I translated the whiteboard sketches into low-fidelity wireframes in Figma to better understand the structure and layout before implementing the app’s data and functionality.

After establishing the wireframes, we began exploring implementation by using AI tools (Cursor) to prototype a functional version of the app.