Computational Design can take advantage of data most companies already have.
Data in product design, is evidence you can act on: Measurements, images, wear patterns, pressure maps, scans, and lab results that describe how athletes move and how products respond.
Product data doesn't just look good on a screen. It helps connect design intent to real performance, so decisions get sharper with every round instead of starting from scratch.
Athlete and biomechanics data — pressure, force, motion, and feedback from wear testing that show how a product behaves.
Material and construction data — how foams, textiles, and outsole compounds perform under load, heat, and repeated use.
Visual data — photographs, scans, annotated details, and existing 3-D models.
Manufacturing and process data — tolerances, tooling limits, and factory feedback that keep concepts buildable as they scale.
Start with what you already capture and rarely reuse: wear-test photos, sample boards, fit sessions, and one-off lab exports sitting in folders nobody searches.
Build scripts to use low-hanging fruit data while building out more robust data collection pipelines.
Focus first on high-frequency decisions — traction, cushioning feel, fit, breathability, moisture wicking...
Building a shared library of visual data means every new product can build on data from the last. This creates an opportunity to document incremental improvements, and the knowledge base stays available across the whole team.
When teams pull from the same data sets, performance improves through better decisions: fewer dead-end samples, clearer mild-to-wild ranges, and faster timelines.
The cost of the product stays the same. What changes is how informed the product becomes.
Want to put this to work on a product? Start a conversation.