Customer moment
Built for operators who want AI to answer high-volume commerce questions without losing source control.
Product
AI Agent for commerce conversations
aserva's AI Agent handles shopper and support questions from product, policy, order, and conversation context.
Operating mode
Assist first
Risk rule
Team approval
Evidence
Sources checked
Product process
AI Agent for commerce conversations in motion
The motion panel shows the operating loop; the board beside it changes by product, use case, or resource.
QuestionSourcesAnswerApproval
AI Agent for commerce conversations workflow board live example
Branch demo
AI Agent for commerce conversations in motion
Customer asks
Can you answer this customer from the store context and show what happens next?
Ticket reasoning stack
Customer ticket
Can you answer this customer from the store context and show what happens next?
Policies
Orders
Conversation history
Selected step
Question
Can you answer this customer from the store context and show what happens next?
Answer, preview, or hand off
Answers can be automated when confidence is high; refunds, exchanges, discounts, and edits stay under team approval.
ProductsPoliciesOrdersConversation historyApproval rules
Interactive example follows the customer moment for this route.
AI Agent for commerce conversations workflow board
01Customer moment
02Needed sources
03Safe response
04Next owner
Use the board to see what changes on this route before scanning.
Most common interaction
Can you answer this customer from the store context and show what happens next?
ProductsPoliciesOrdersConversation historyApproval rules
aserva response path
aserva reads products, policies, order or channel context, then gives the operator a controlled answer or action preview.
Answer, preview, or hand off
Answers can be automated when confidence is high; refunds, exchanges, discounts, and edits stay under team approval.
Why this page matters
What changes on this route.
Source plan
Connect catalog, policy, order, and conversation data before the agent writes back.
Control model
Answers can be automated when confidence is high; refunds, exchanges, discounts, and edits stay under team approval.
Measure it
Track resolution rate, source coverage, handoff reasons, and assisted conversion signals.
What this page covers
aserva's AI Agent handles shopper and support questions from product, policy, order, and conversation context.
Built for operators who want AI to answer high-volume commerce questions without losing source control.
The target outcome is faster answers, fewer repetitive tickets, and clearer revenue moments from support.
The workflow is tied to a customer moment, a source set, and a safe next step.
Commerce context
aserva starts from the system that owns the truth, then adds the conversation context around it.
Connect catalog, policy, order, and conversation data before the agent writes back.
Customer questions, policy rules, order state, and product data stay visible together.
If a source is missing or confidence is low, the human handoff path stays explicit.
Control model
The product principle stays consistent across verticals, channels, and comparisons.
Answers can be automated when confidence is high; refunds, exchanges, discounts, and edits stay under team approval.
Sensitive changes are prepared as previews before execution.
Operators see what the AI read, what it wants to say, and why the action is safe or blocked.
Proof and measurement
The route shows what has to be measured before the workflow expands.
The proof focus is source-backed answer quality plus safe escalation behavior.
Track resolution rate, source coverage, handoff reasons, and assisted conversion signals.
Expansion happens by channel, workflow, and action type after evidence is visible.
Operating workflow
Source. Answer. Action.
The control model stays consistent, but the sources and customer moment change by page.
Proof boundary
What this route proves.
Audience, outcome, and measurement stay visible so the page does not drift into generic claims.
Audience
Built for operators who want AI to answer high-volume commerce questions without losing source control.
Outcome
The target outcome is faster answers, fewer repetitive tickets, and clearer revenue moments from support.
Measurement
Track resolution rate, source coverage, handoff reasons, and assisted conversion signals.
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