Fast-changing catalog
Products, variants, attributes, price, promotion, availability, and content change across channels and regions.
Commerce journey
Make every product decision more relevant.
We build commerce systems that help customers find the right product and help teams place, price, and present inventory with stronger demand context—across web, mobile, stores, and service.
Discuss a real use caseLive product model
01
Intent
02
Catalog
03
Availability
04
Recommendation
05
Basket
Where the work breaks
Retail AI must balance relevance, availability, margin, brand rules, and customer trust. We connect discovery and operations so the experience never recommends what the business cannot fulfill.
Sparse intent
Most sessions reveal little context, while new products and customers lack behavioral history.
Operational coupling
Discovery, demand, inventory, fulfillment, returns, and service decisions affect one another.
Systems we can build
Customer-facing products connected to retail reality.
Discovery, personalization, merchandising, service, and inventory share the same product and availability truth.
PRODUCT 01
Product discovery and search
Combine semantic intent, catalog attributes, availability, and business rules to rank useful products and explain refinements.
DeliversRelevant, filterable result set
PRODUCT 02
Guided shopping assistant
Translate conversational needs into product constraints, compare viable options, and avoid unsupported product claims.
DeliversEvidence-grounded shortlist and comparison
PRODUCT 03
Merchandising and recommendations
Generate candidate sets and rank them against customer relevance, margin, inventory, campaign, diversity, and brand constraints.
DeliversContextual assortment with reasoned controls
PRODUCT 04
Demand and inventory intelligence
Forecast at the decision grain, quantify uncertainty, and turn exceptions into allocation, replenishment, or markdown review.
DeliversForecast distribution and action queue
Product spotlight
A shopping assistant that can finish the job
It understands intent, compares compatible products, respects price and availability, builds a basket, and hands off edge cases with the conversation intact.
Catalog and taxonomySearch and browse behaviorInventory and eligibility
Decision workspace
1IntentReady
2CatalogReady
3AvailabilityReady
4RecommendationReview
Who it serves
Two sides of the same commerce system.
Shoppers
Relevant discovery, honest availability, and confident comparison.
Merchandisers
Explainable ranking controls and content-gap detection.
Store teams
Local inventory, task priority, and customer context.
Service teams
Order, product, policy, and resolution options together.
Existing ecosystem
Composable across storefront, PIM, OMS, ERP, and CRM.
We preserve systems of record and add a permissioned product layer for retrieval, orchestration, review, and action.
01Commerce and OMS platforms
02PIM and DAM
03Search and personalization
04POS and store inventory
05CRM and service tools
06Warehouse and analytics platforms
Product safeguards
Controls designed for retail & ecommerce work.
01
Claim grounding
Only generate product statements supported by approved catalog, policy, or supplier content.
02
Customer choice
Honor consent, deletion, channel preferences, and clear boundaries around personalization.
03
Ranking governance
Make sponsored, commercial, availability, and diversity rules explicit and testable.
04
Experiment guardrails
Monitor returns, cancellations, complaints, page performance, and subgroup effects alongside conversion.
What improves
Measured in the operation.
01DiscoverySuccessful searches, refinement depth, and zero-result rate
02RelevanceQualified engagement and add-to-cart after recommendation
03QualityReturns, cancellations, and unsupported-answer rate
04InventoryAvailability, stockout, overstock, and aged stock
05ForecastingBias and error at the replenishment decision grain
06EconomicsIncremental margin after fulfillment and return costs
Before you build
Questions about relevance, control, cold start, and customer data.
Can an AI shopping assistant use live inventory and price?+
Yes. Product answers should call authoritative commerce services at request time for volatile facts such as price, promotion, eligibility, and availability.
How do you prevent made-up product claims?+
We constrain retrieval to approved product content, require source-backed attributes, validate output schemas, and use deterministic checks for regulated or high-risk claims.
Will recommendations only optimize conversion?+
Not unless that is the chosen objective. We typically balance qualified engagement, margin, availability, diversity, returns, customer value, and brand constraints.
Can the platform support stores as well as ecommerce?+
Yes. The product, customer, and inventory intelligence layers can serve associate tools, kiosks, clienteling, store fulfillment, and service journeys as well as digital channels.
Bring us the hard part