Product discovery that understands catalog, inventory, content, and customer intent.
IMV Search Commerce helps shoppers search, browse, compare, and discover products using the same operating data that powers ecommerce, stores, fulfillment, and AI-ready catalog answers.
"waterproof jacket for travel pickup today"
Ranking uses shopper intent plus inventory, content, margin, fulfillment promise, and merchandising rules.
Search fails when it knows keywords but not retail reality.
Traditional search can retrieve products, but retailers need discovery that understands intent, availability, content quality, business rules, and what can actually be fulfilled.
Use the retail operating graph to enrich search, browse, recommendations, and AI answers with product, inventory, content, and order context.
Keyword-only matching
Shoppers describe use cases, constraints, and intent, not just exact product names.
Unavailable winners
Top-ranked items can frustrate shoppers if they cannot be picked up, shipped, or transferred.
Merchandising friction
Teams need controls for boosts, bury rules, campaigns, synonyms, redirects, and recommendations.
Weak feedback loops
Queries, clicks, conversions, no-results, and fulfillment failures should teach the system.
Search, browse, recommendations, and merchandising in one operating context.
Search Commerce should work across the full discovery journey, from typed queries to collection browsing to guided product answers.
Intent-aware search
Map natural language, synonyms, attributes, and product facts to sellable results.
Browse and filters
Use structured attributes, availability, price, fit, brand, location, and product state.
Recommendations
Recommend substitutes, complements, bundles, next-best products, and nearby alternatives.
Merchandising controls
Boost, bury, pin, redirect, campaign, suppress, and explain result behavior.
The best result is not always the item with the best text match.
IMV can rank with operational context: local availability, pickup promise, transfer feasibility, margin, returns risk, content quality, and merchandising strategy.
See inventory truthIntent score
Relevance across query, product facts, attributes, content, and shopper context.
Promise score
Pickup, ship, transfer, nearby store, backorder, or substitution confidence.
Business score
Margin, campaign goals, inventory position, seasonality, and merchandising rules.
Experience score
Content quality, returns risk, reviews, support answers, and product guidance.
Merchandising workbench
Give merchants controls without forcing them into developer tickets.
Merchandising teams need to shape discovery outcomes while still respecting inventory, fulfillment, customer experience, and finance realities.
Review merchandising controlsProduct questions should return useful answers, not just result grids.
As shoppers ask more specific questions, discovery needs product facts, buying guides, policy content, store availability, and safe next steps.
Ask
Understand need, constraints, use case, location, and timing.
Compare
Explain differences using attributes, content, inventory, and policy data.
Act
Guide pickup, shipping, store visit, quote, support, or assisted handoff.
Every query should teach the retail operating layer.
Search behavior can reveal demand, content gaps, assortment issues, stock problems, and merchandising opportunities.
Query
Capture intent, language, filters, location, and shopper context.
Result
Measure clicks, result quality, no-results, zero-stock wins, and sort behavior.
Action
Adjust synonyms, content, ranking, merchandising, catalog data, or inventory strategy.
Outcome
Track conversion, basket, pickup promise, order status, returns, and customer satisfaction.
Search Commerce gets stronger when it is connected to the whole platform.
Discovery should read from product content, inventory, ecommerce, and AI catalog services instead of relying on isolated indexes.
Find where shoppers are searching but your catalog is not answering.
We can map queries, filters, recommendations, no-results, content gaps, inventory promises, and AI answer readiness to show where IMV can improve discovery.