WE KEEP RESIZING CLOTHES.
WHAT IF WE RESIZED THE PROBLEM?
The world doesn’t have a sizing crisis.
It has a fabric crisis.
For years, we’ve been looking in the wrong direction.
We’ve chased virtual try-ons, body scans, and AI fit prediction.
But none of them address the root cause:
Every roll, every batch, every supplier, a new variable.
And every variation multiplies across millions of garments.
Fabric performance inconsistency.
A single thread can change everything.
One millimetre of stretch.
One fraction of shrinkage.
One unpredictable fabric roll.
Multiply that by global production, and you get billions lost, not because of style or size, but because the material won’t behave the same way twice.
“WE BUILT PRECISION PATTERNS ON UNPREDICTABLE FOUNDATIONS”
From mill to landfill, the chain reaction of inconsistency.
When fabric performance fluctuates, everything downstream breaks:
Patterns distort, fit fails, returns rise, waste grows.
It’s not just inefficiency, it’s erosion.
FAQs
Why does fashion still struggle with sizing and fit?
Because the root issue isn’t size charts, it’s the inconsistency of fabric performance. Two materials with identical specifications can behave differently once produced, leading to variation in drape, stretch, and shrinkage. Until this is standardised, fit will always remain inconsistent.
What causes fabric inconsistency across suppliers or seasons?
Fabric differences stem from changes in raw materials, mills, processes, and finishing methods. Even subtle variations affect how garments perform in real life. These inconsistencies compound across supply chains, creating unpredictable fit outcomes at scale.
Why can’t PLM or ERP systems solve the fit problem?
Traditional PLM and ERP tools manage documentation, not data quality. They don’t measure or benchmark the physical behaviour of fabrics, the true source of variation. Without fabric intelligence, digital workflows simply replicate legacy inconsistencies faster.
How does AI help improve fit and sizing accuracy?
AI enables the analysis of large-scale material data, from lab test results to production feedback, to identify trends and anomalies invisible to the human eye. By applying machine learning, brands can predict performance and prevent fit issues before production.
What is fabric intelligence?
Fabric intelligence is the ability to measure, analyse, and standardise fabric performance using structured data. It replaces subjective “hand feel” with verified insight, allowing brands to make fit decisions based on science, not intuition.
How does TAILR address the root cause of sizing issues?
TAILR doesn’t just digitise the process, it stabilises it. By building consistency into fabric data and linking it to development, sourcing, and manufacturing, TAILR ensures every style starts with reliable inputs, reducing returns, waste, and rework.
Who benefits most from this approach?
Brands, retailers, and manufacturers seeking to reduce returns, standardise fit, or scale production across regions. TAILR supports everyone from emerging labels to global enterprises looking to embed quality and repeatability in their workflows.
Is this technology difficult to adopt?
Not at all. TAILR integrates seamlessly into existing workflows. Our platform complements, rather than replaces, your existing PLM or ERP setup, meaning teams can start small, learn fast, and scale efficiently across product categories or divisions.