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.
Current posture
This solution is supported by current product proof and is actively marketed as a live capability.
In-product proof
What product intelligence software looks like in the current product.
The public story now moves straight into route-backed proof so the claim stays tied to how the workflow actually behaves.
North-star pages use current foundation routes as proof, not hypothetical product surfaces.
MerchandisingProduct Relationships
The product graph turns item knowledge into a reusable decision asset.
Relationship signals, product similarity, and item context strengthen pricing, recommendation, and assortment decisions across the operating system.
Mapped items
150 SKUs
Catalog context is connected to relationship and similarity signals.
Strong links
42 pairs
High-confidence relationships ready to influence recommendations.
Refresh jobs
1 ready
Relationship generation and graph refresh stay inside the workflow.
Relationship highlights
top 2Similar product cluster needs pricing review
WatchBetter product understanding improves commercial decisions downstream.
Relationship strength changed after a catalog update
StableProduct truth compounds through the rest of the system once the item graph improves.
Step 1
Catalog input
Step 2
Relationship graph
Step 3
Recommendation context
Step 4
Commercial action
Outcome
Product intelligence becomes the foundation for recommendation quality, competitor context, and better downstream decisions.
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 ItemIQ.
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.
No. The point is not passive storage. Zerqano uses product intelligence as a working layer for pricing, recommendation, and planning decisions.