Your stores generate millions of signals. y helps you turn them into decisions.
These aren't hypotheticals. These are the numbers keeping retail leaders awake right now.
y doesn't just show you data. It cross-references store performance, competitor intelligence, industry research, and macroeconomic trends — then tells you what to do about it.
Conversion dropped 8%? y doesn't just flag it. It checks queue wait times (22min vs 8min normal), cross-references staffing levels, finds that checkout #3 was unstaffed 12–2pm, and cites research showing >15min waits cause 18% conversion loss.
y monitors competitor websites with AI-powered change detection. When a rival launches a new promotion, changes pricing, or redesigns their layout, you know within hours — with a change score and strategic analysis.
Which display gets the most attention? Which zone has high traffic but people walk right through? y tracks every area of your store — who enters, how long they stay, what catches their eye — per floor, per zone, per hour.
Was last month's traffic dip your problem, or the whole market? y overlays EU-wide economic data — consumer confidence, retail spending trends, and e-commerce shifts across 30+ countries. Your numbers in context.
Before you even open your laptop: yesterday's traffic, conversion, and revenue. Which stores hit targets. Which didn't and why. New reviews with sentiment. Weather-adjusted forecasts for today. Staffing recommendations based on predicted peak hours.
When y recommends a change, it cites its sources. The retail knowledge base contains thousands of evaluated research documents across 8 categories — from shrinkage prevention to staff scheduling to omnichannel strategy.
y adapts to your vertical, your role, and your language.
Telecom stores lose customers to 40-minute activation processes. y tracks queue wait times by service type, predicts peak load, and recommends staffing shifts — while monitoring if competitors just dropped a new trade-in promo.
93% of luxury houses can't hire enough store managers. The ones you have need to focus on high-value interactions. y identifies which zones have high foot traffic but low dwell time, and which displays aren't being noticed.
At 1.6% net profit, every operational inefficiency hits the bottom line. y correlates checkout queue lengths with abandonment, tracks peak hour patterns, and benchmarks your conversion against industry research.
35% of banks are expanding branch networks while shifting to advisory models. y compares foot traffic, transaction mix, dwell time, and NPS to identify which branches should go advisory vs. full-service.
You know which store is underperforming. You don't know why. y cross-references traffic (up or down?), conversion (staff issue?), reviews (what are customers saying?), and market data (macro effect?) to give you the answer in one message.
Shrinkage costs $112B/year. ORC is up 26.5%. y flags anomalous patterns: unusual short-visit spikes in specific zones, sudden changes in browsing-to-purchase ratios, and time-of-day patterns that precede known theft scenarios.
y is in early access. We're onboarding retail teams who want to be first to have an AI that actually knows their stores.