Martin Hejda

Martin Hejda is a builder drawn to gaps — the kind of spaces where useful technology clearly should exist but doesn't yet. His path to DimensionsPot started with a straightforward observation: body data is relevant across a wide range of products and verticals, yet the tooling for working with it programmatically was either absent, inaccessible, or wrapped in complexity that made adoption impractical for most teams. Fitness apps, e-commerce platforms, healthcare tools, workplace safety systems, sports equipment configurators — the same unmet need kept appearing across different contexts.

DimensionsPot is the result of that observation taken to its practical conclusion. A single API call — height, weight, gender — returns 130 body dimensions calibrated across regional body profiles, with a separate pediatric engine for age-based predictions. The architecture is stateless by design: no photos, no PII, no data retention. For teams building across fitness, sizing, healthcare, ergonomics, gaming, or workwear procurement, it's infrastructure that makes body dimension data a standard part of the stack rather than a one-off engineering problem.

The ambition behind it is straightforward: make body dimension data as accessible to developers as any other API call, for any product where the human body matters.