Case StudyFast-growing fashion retail · 750K SKUs
Every launch was
a data race
they kept losing.
A fashion brand dropping new collections every 4–6 weeks was spending that time fixing data instead of selling. Manual QA before every launch. Campaigns delayed. The catalog couldn’t keep pace with the brand.
RESULTS AT A GLANCE
Within 90 days
Speed to live
Faster catalog ingestion-to-live time
Products went from feed to site in significantly less time
Launch reliability
No more launch delays waiting on data to be cleaned
Campaign impact
Marketing could execute on time, every time
Automation rate
SKUs through without manual intervention
Team redirected from QA to higher-value work
New collections hit the site ready to sell — not stuck in a data validation backlog
Supplier feed inconsistencies caught and corrected automatically before they reached the catalog
Only genuinely uncertain SKUs escalated for human review — the rest moved through without touching a person
Catalog quality became a guaranteed output of the pipeline — not something the team had to chase after every launch
The problem
The catalog couldn’t keep pace
with the brand.
At roughly 750,000 SKUs with new collections every 4–6 weeks, this brand’s launch velocity was a competitive strength — except that every launch triggered a manual data cleanup cycle. Supplier feeds arrived inconsistently. Attributes were missing or conflicting. Category mappings varied across sources. Before anything could go live, someone had to fix it.
The bottleneck wasn’t creative or commercial. It was data. And it was happening on the same schedule as every seasonal drop, every campaign, every restock.
What was slowing launches down
- High SKU velocity from drops every 4–6 weeks — volume the team couldn’t manually keep up with
- Inconsistent supplier feeds across categories — different formats, different standards, every time
- Missing, incorrect, or conflicting attributes arrived with every product batch
- Category mappings varied across sources — no consistent structure the pipeline could rely on
The operational cost
- Every launch required a manual validation pass before products could go live
- Campaigns were delayed while the team waited on data to be corrected
- Errors that slipped through caused downstream corrections in search, ads, and feeds
- Team capacity was being consumed by data firefighting — not catalog strategy
01 — Shift in shopper behaviour
Queries became intent-rich
- Shoppers used occasion and aesthetic language: “airport outfit”, “elevated basics”
- Search relied on category + attribute matching — not intent
02 — Catalog signal gap
No vocabulary for how shoppers searched
- No structured fields for occasion, aesthetic, or use-context
- Products lacked intent-aligned descriptors entirely
03 — System limitation
Engine could only rank what existed
- Search could only rank on available signals
- Query relevance plateaued despite strong baseline conversion
04 — Observed symptoms
The ceiling showed up in the data
- Engagement on search flattened
- Users refined queries more frequently — a sign results weren’t landing
- Revenue growth from search slowed
The intervention
Catalog quality built
into the pipeline itself.
Perspiq.ai was introduced as a validation and enrichment layer inside the ingestion pipeline — applied before products reached the site, ads, or any downstream feed. The goal: make catalog quality a guaranteed output of the system, not a manual step the team had to complete before every launch.
Stage 01
Attribute verification at ingestion
- Visual attributes extracted from product imagery and cross-checked against supplier data
- Mismatches, missing fields, and inconsistencies detected automatically — before they entered the catalog
- Every SKU checked against the same standard, regardless of which supplier it came from
Stage 02
Structured enrichment
- Missing attributes generated and aligned to schema — gaps filled before they became problems
- Attribute expression standardised across categories — consistent language across the full catalog
- Every SKU brought to the same data depth regardless of source or category
Stage 03
Confidence-based routing
- Each SKU assigned a confidence score based on data completeness and consistency
- High-confidence SKUs auto-approved for publishing — no human touch needed
- Low-confidence SKUs flagged for managed review — only the ones that genuinely needed attention
Stage 04
Pre-publish readiness check
- Every SKU validated before entering site, ads, or feeds — no downstream corrections needed
- Minimum data quality thresholds enforced at the gate, not discovered after launch
- Downstream correction cycles eliminated — what went live was ready
The shift: instead of the team chasing data quality before every launch, the pipeline guaranteed it. Human attention went to the SKUs that actually needed it — not to checking everything just in case..
"Catalog quality stopped being something the team had to achieve before every launch. It became something the system delivered automatically."
Why Perspiq.ai
Catching attribute errors at ingestion and generating missing fields accurately requires a model that understands fashion data at the attribute level — not a generic validation tool that flags everything or misses category-specific nuance. Perspiq.ai is trained on 900,000+ verified retail taxonomy attributes from real brand data, which is why confidence scores are reliable enough to automate 90%+ of SKUs without sacrificing accuracy. 95% accuracy from day one. No training period on your catalog required.
Next step
See how fast your next launch
could actually move
A 30-minute demo or a catalog audit on your own SKUs. We’ll show you exactly where your ingestion pipeline is losing time — and what fixing it looks like.
Topics
ecommerce launch operations
product data validation
fashion seasonal launches
catalog quality automation
supplier feed inconsistency
pre-publish data readiness
catalog enrichment at scale
AI-powered catalog enrichment with expert oversight—delivering shopper-ready data that feeds search and SEO.
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