Buyer problem
Teams cannot price, forecast, recommend, or compare products confidently when the underlying product data is incomplete or disconnected.
Product intelligence
Available nowKnow your products, enrich weak data, and build the foundation for better decisions.
Zerqano helps teams understand product context, competitor positioning, and relationship signals so downstream pricing, stocking, and recommendation decisions start from stronger product truth.
Product Relationships
8 products10 connections
What the graph means
Find products customers already buy together, then turn the strongest link into a reviewed bundle.
Bundle offer
Earbuds + case
82% co-purchase confidence
Campaign lift
+14%
Expected revenue per cart
Next step
Approve
Create bundle and email merch team
Cross-sell insight
Customers buying Wireless Earbuds Pro also buy Phone Case Ultra 82% of the time. Consider bundling for a 14% revenue lift.
Approve bundleAdd to campaignSend to merch team
Visual walkthrough
What this workflow looks like inside the product.
Start with the primary module for this solution, then view how it connects to command and inventory context.
Product Relationships
8 products10 connections
What the graph means
Find products customers already buy together, then turn the strongest link into a reviewed bundle.
Bundle offer
Earbuds + case
82% co-purchase confidence
Campaign lift
+14%
Expected revenue per cart
Next step
Approve
Create bundle and email merch team
Cross-sell insight
Customers buying Wireless Earbuds Pro also buy Phone Case Ultra 82% of the time. Consider bundling for a 14% revenue lift.
Approve bundleAdd to campaignSend to merch team
Command Center
Condensed live preview
Monthly Revenue
$0
+12.4%
Stock Health
0.0%
3 alerts
Top action
SKU-4821 — Below safety stock
SKU-4821 — Below safety stock
Procurement
Vendor lead time changed +3 days
Inventory
Open Command CenterGenerate PO
zerqano.com/product-intelligence-main
LIVE
Product Relationships
8 products10 connections
What the graph means
Find products customers already buy together, then turn the strongest link into a reviewed bundle.
Bundle offer
Earbuds + case
82% co-purchase confidence
Campaign lift
+14%
Expected revenue per cart
Next step
Approve
Create bundle and email merch team
Cross-sell insight
Customers buying Wireless Earbuds Pro also buy Phone Case Ultra 82% of the time. Consider bundling for a 14% revenue lift.
Approve bundleAdd to campaignSend to merch team
What product intelligence software looks like in the current product.
Operator-ready workflow
What the user reviews, clicks, and sends from this solution.
The page preview is tied to a practical review queue so visitors understand the day-to-day action, not only the category name. Every step keeps the human approval moment visible.
Verified option 01
Review bundle candidate
See the products customers buy together and the confidence behind each link.
Verified option 02
Notify merchandising
Send the recommended campaign and product set to the owner for review.
Verified option 03
Track lift
Watch cart attach rate and revenue lift after the bundle goes live.
Review before action
The AI earns trust inside this workflow before it earns automation.
Every product intelligence software recommendation carries its reasoning, waits for approval, and keeps the operator in control. That makes the workflow safe to adopt before the team is ready to automate more of it.
Every action is reasoned
Before the agent suggests anything, it shows you the why — the model, the data, the confidence, and which row it came from.
Nothing happens without you
No silent writes. Every change to inventory, pricing, POs, or documents waits behind a one-click approval — even when you sleep.
Undo for 60 seconds
Every approved action is reversible. The agent shows a fading undo pill so a mis-click is never a problem.
Your data, your lane
Row-level security and per-skill data isolation means your numbers never bleed into another tenant, and the agent only reads what it needs.
Problem framing
Why this workflow breaks today.
Product context is still scattered across catalog exports, competitor sheets, and one-off enrichment work.
Merchandising, catalog operations, pricing, and commercial teams managing broad product assortments.
Product data is incomplete when decisions are made
Missing attributes, weak descriptions, and inconsistent structure make planning and pricing work much harder than it should be.
Competitor context sits outside the operating loop
Commercial teams often track competitor pricing and product overlap in separate files that never flow back into daily decisions.
Recommendation quality depends on product truth
Cross-sell, pricing, and website recommendations weaken quickly when the item graph is thin or inconsistent.
Current proof
What exists now
- - Map product context into a shared operating layer instead of separate files and tabs.
- - Support competitor-aware pricing and assortment review with connected product context.
- - Strengthen relationship, cross-sell, and recommendation workflows with better item understanding.
- - Connect enriched product truth to pricing, demand, and recommendation decisions.
Operational proof
- - Supports product uploads, relationship mapping, and competitor-aware pricing workflows.
- - Acts as the backbone for cross-sell intelligence and future-facing recommendation experiences.
- - Keeps product context attached to commercial and operational decisions instead of isolating it in back-office cleanup.
Trust and explainability
- - Product context is not hidden behind a single black-box score. Teams can review the product, the relationship signals, and the downstream effect.
- - Competitor-aware and relationship-aware decisions stay attached to the route where they will actually be used.
- - The product layer compounds value because stronger item truth improves pricing, forecasting, and recommendation quality together.
Connected system
This workflow gets stronger because it is connected to the rest of Zerqano.
01
Upload or connect catalog and enrichment inputs.
02
Review product context, gaps, and related-item signals.
03
Route the improved product truth into pricing, cross-sell, and planning workflows.
04
Keep the item graph connected as downstream decisions evolve.
Where it expands next
Expands into stronger competitor mapping, richer enrichment loops, upsell recommendations, and customer-facing recommendation delivery.
Connected modules
Related solution pages
Pricing intelligence
Pricing Intelligence Software
Turn pricing from a disconnected spreadsheet debate into a governed operating workflow with margin context.
Cross-sell intelligence
Cross-sell Intelligence Software
Use relationship-aware intelligence to increase basket value and improve assortment decisions with better product pairing context.
Demand intelligence
Demand Intelligence Software
Use forecast-backed demand signals to guide inventory, procurement, and pricing decisions with less guesswork.
FAQ
Questions teams ask during evaluation.
Get answers about how Zerqano handles product intelligence software and the workflows that connect to it.
No. The point is not passive storage. Zerqano uses product intelligence as a working layer for pricing, recommendation, and planning decisions.