From inventory checks to settlement analysis, in a single question
Connects market reviews and policy documents with sales, inventory, and settlement databases, turning back-office data analysis into self-service.
At a glance
One-sentence definition
A retail and commerce solution that connects sales, inventory, reviews, and settlement data so teams can trace sales anomalies, VIP churn, and returns in natural language.
Who it is for
Merchandisers, settlement and CRM teams, and e-commerce data teams.
Problem it solves
Weekend sales, partner settlements, and review issues live in different systems, so root-cause work takes a day.
Key capabilities
- ·Category sales and inventory cross-analysis
- ·Partner settlement verification
- ·VIP churn risk and return-rate diagnosis
Environment, integrations, deployment, security
Order, product, and review databases plus settlement files. Browser demo at /demos/retail and Qurify production.
Limitations
The public demo uses fictional data. A production rollout requires connecting the customer's data.
What we build first
A screen that connects settlement, inventory, and review data, showing the cause of settlement gaps and stock anomalies with evidence
Challenges
Retail & Commerce: Today's Challenges
Complex Settlement & Fee Structures
Per-partner fee rates and promotion cost-sharing are intertwined, making settlement verification take days.
Missed VIP Churn Signals
Purchase history and support records live in separate systems, so churn signals from key customers go unnoticed.
Slow Returns & Stockout Analysis
Understanding a spike in returns or the cause of a stockout requires manually cross-referencing data across systems.
Use Cases
Questions teams ask
Q.“Show me last weekend's luxury section sales versus the week before”
Category and period conditions are interpreted in business terms and returns the sales comparison instantly.
Q.“Verify this month's partner settlement amounts”
Returns settlement verification results reflecting fee rates and promotion cost-sharing rules, with the full calculation basis.
Q.“Which VIP customers are at high risk of churning?”
Combines purchase cycle changes with support history to flag at-risk customers and suggest actions.
Impact
What changes
Settlement verification
Who runs the analysis
Churn response
Reference — Natural-language data query (NL2SQL) deployment experience with an e-commerce company
Related pages
Retail & Commerce — want to see it with your own data?
Tell us one task you want solved and where the data lives, and we will decide together where to start.