10–25%
Search-Driven Revenue
Search-Driven Revenue
Lift in revenue influenced by search
Shopper searches “quiet luxury blazer”
finds your relaxed-fit tailored blazer. That's revenue, not just traffic.
Give your catalog perspective — speak the language your shoppers use.
AI-powered catalog enrichment with expert oversight—delivering shopper-ready data that feeds search and SEO.
60-85%
Zero-Result Searches
Zero-Result Searches
Reduction from understanding shopper language
Customer searches “terracotta knit sweater”
we match it to your rust-colored cable knit, even though you tagged it Orange.
3-5x faster
Enrichment Speed
Enrichment Speed
24-hour SLA for standard catalogs
Upload 5,000 products today
AI enriches, experts verify, delivering enriched data back within 24 hours.
20-35%
Precise Product Matches
Precise Product Matches
Increase in long-tail and seasonal discoverability
“Black midi dress for work”
finds your tailored sheath dress — not every black dress in your catalog.
The Problem
Your shoppers can't find what you stock. And it's costing you revenue.
60% of searches return zero results – even when the products exist in your catalog – which means you're losing 18-24% of potential search-driven revenue to broken discovery.
Example
Shopper Searches
"cozy cable knit sweater"
Your System
0 results
Your Actual Inventory
73 matching SKUs
Every failed search is abandoned revenue.
Why this keeps happening
The context shoppers actually use is missing. Your catalog has basic data – category, color, material. But shoppers don't search that way. They search for:
-
Trends
“Cottagecore,” “Quiet Luxury,” “Y2K”
-
Occasions
“Date Night,” “Workwear,” “Vacation”
-
Moods
“Romantic,” “Edgy,” “Minimal,” “Bold”
-
Style Signals
“Relaxed fit,” “Structured,” “Flowy”
Our View
Fashion isn't a classification problem. It's a context problem.
Most systems identify objects. Fashion teams interpret meaning.
Generic AI sees: "dress"
Fashion teams see: "midi shirtdress with relaxed fit, desk-to-dinner versatility, quiet luxury aesthetic, effortless workwear styling"
The gap: AI sees objects. Fashion teams see context, trends, occasions, and mood.
Why Perspiq.ai
Built for fashion retail, not generic AI
Most AI is trained on internet images. We're trained on real fashion catalogs—4,500+ categories, 900,000+ retail attributes from actual brand taxonomies.
What makes us different
Perspiq.ai
- Deep expertise in AI driven product discovery
- Trained on 900K+ retail attributes from real fashion catalogs
- 50M+ assets enriched over two decades using human experts
- Captures trends, occasions, moods (“quiet luxury,” “date night,” “effortless”)
- Brand-safe tonality, descriptions sound like your brand
- Proven operational bandwidth to handle client demands at scale, without delays
- Works with your existing stack through API and Cloud integration
Fashion AI Alternatives
- Trained on generic internet images
- AI outputs dumped into your catalog—you clean up the mess
- Basic attributes only (“dress,” “blue,” “cotton”)
- Generic robot copy that sounds like Amazon
- Requires months of training on your data
- Platform replacement or heavy customization required
- Limited deployments, unproven at catalog scale
The result: Better discovery → more findable products → higher revenue.
How It Works
From product image to enriched catalog
Move the slider to see the Impact of our Enrichment
$
%
Revenue you could recover
+$0 / SKU$0
Avg. $50 AOV · 2.2% base CVR
Conversion Rate Uplift
vs generic AI+0.0%
Cart Completion Lift
fewer exits+0.0%
Works with what you already have
Designed to layer into your existing stack
Commerce Platforms
- Shopify
- Salesforce Commerce Cloud
- BigCommerce
- Adobe Commerce
Search Engines
- Algolia
- Elasticsearch
- Coveo
- Bloomreach
Data Pipelines
- REST API
- GraphQL
- Webhooks
- CSV/FTP
Workflow Tools
- PIM Systems
- DAM Tools
- MDM Solutions
Don't see your platform?
Talk to our Integration Team
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