Make retail AI useful by grounding it in products, policies, inventory, orders, and governed actions.
IMV gives AI commerce experiences the operating context they need to answer accurately, recommend responsibly, and hand off safely.
Grounded answer path
AI shopping experiences fail when they are disconnected from retail reality.
A model can sound confident while missing inventory, pricing, policy, eligibility, order state, or margin context.
Expose governed retail facts and action boundaries so AI can be useful without inventing promises the business cannot keep.
Hallucinated promises
AI may suggest products, policies, availability, or delivery options that are not valid.
Missing constraints
Recommendations need fit, inventory, price, margin, eligibility, and customer context.
Unsafe actions
Refunds, discounts, substitutions, and quotes need policy and approval boundaries.
No feedback loop
AI interactions should improve search, content, catalog, support, and merchandising.
Ground AI in the retail operating graph.
AI commerce is not just a chat box. It is a controlled interface into product, inventory, content, policy, order, and customer context.
Product facts
Descriptions, attributes, compatibility, variants, bundles, pricing, and merchandising guidance.
Availability truth
Location stock, pickup promise, delivery eligibility, reservations, and substitutions.
Policy knowledge
Returns, warranties, service rules, payment terms, B2B rules, and escalation paths.
Action controls
Allow, deny, approve, hand off, quote, recommend, or escalate based on context.
From shopper intent to governed action.
The experience should be conversational, but the backend needs explicit retrieval, policy, and action design.
Classify intent
Detect whether the user needs discovery, comparison, availability, policy, support, reorder, quote, or escalation.
Retrieve facts
Pull approved catalog, inventory, content, order, customer, and policy context.
Constrain action
Apply role, eligibility, pricing, margin, compliance, and business rules before acting.
Measure impact
Track conversion, deflection, no-answer gaps, policy conflicts, and content improvement needs.
AI commerce depends on search, catalog APIs, knowledge, and ecommerce execution.
hhe agent should use the same truth the site, stores, support, and operations use.
Make AI helpful, auditable, and safe to scale.
Retail AI needs visibility into sources, confidence, actions, and outcomes.
Source traceability
Show which product, policy, content, or order fact supported an answer.
Policy enforcement
Prevent unsupported returns, discounts, promises, substitutions, and quotes.
Human handoff
Escalate low-confidence or high-risk moments to store, support, sales, or finance teams.
Feedback capture
Use unanswered questions and bad matches to improve content, catalog, and search.
Build AI commerce on governed retail facts, not generic responses.
We can map the product, policy, catalog, knowledge, and action contracts your AI experiences need before launching them to shoppers or staff.