The data layer
fast fashion
never published.
Apparel Signals collects, normalizes, and structures fiber composition, certifications, pricing, and production geography across major European retailers — and makes it queryable. The data exists; it's just buried in inconsistent product descriptions across five different languages.
Structured intelligence
from unstructured product data.
Every fast-fashion product page contains a material declaration — but "polyester," "polyester (PES)," and "poliéster" are three different strings for the same fiber. Multiply that by 400+ material types, hundreds of retailers, and thousands of SKUs per season, and you have a dataset that is publicly available but practically unusable without a normalization layer.
Apparel Signals is that layer. We collect product data at API level, run multi-strategy alias resolution against EU Regulation 1007/2011 nomenclature, and surface structured analytics — fiber origin, certifications, pricing, production geography — at variant-level granularity.
Multiple retailers.
One structured view.
| China | 34% | |
| Bangladesh | 22% | |
| Turkey | 14% | |
| Portugal | 10% | |
| India | 8% | |
| Other | 12% |
Built for people who
need the numbers,
not the press releases.
Sustainability reports tell you what brands want you to believe. Product-level material data tells you what they actually made. There's often a significant gap between the two — and that gap is where the signal lives.
Where does your material
intelligence practice actually stand?
15 questions across normalization, analysis, and competitive awareness. You'll get a score, three specific insights, and a tailored next step.
Take the Free Assessment →