Help shoppers find a suitable product through conversation

Ask about need, budget, preferences and constraints, then guide discovery using approved catalogue information and configured business rules.

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Filters do not capture the whole buying question

  • Shoppers describe occasions, preferences and constraints that do not match catalogue labels.

  • Large result sets make the shopper compare too many loosely related options.

  • A recommendation without a clear reason is hard to evaluate or trust.

  • Missing product data can turn a confident answer into the wrong recommendation.

How conversational product discovery narrows the choice

01

Understand

Ask the minimum useful questions about the shopper's intended use, budget, preferences and constraints.

02

Retrieve

Use approved catalogue fields and business information available to the configured workflow.

03

Narrow

Present a manageable set of suitable options instead of returning an undifferentiated catalogue page.

04

Explain

State which known attributes match the shopper's request and where the options differ.

05

Escalate

Bring in staff when information is missing, the request needs judgment or no supported match is available.

Give shoppers a reason to continue

Natural-language needs

Let shoppers describe the outcome, recipient, use case, size, compatibility requirement or feature they want to avoid.

ecommerce AI agent

Catalogue-grounded answers

Base guidance on the product information, availability fields and business rules supplied to the configured workflow.

approved product knowledge

Clear option differences

Explain relevant attributes and tradeoffs so the shopper can compare a few suitable choices without guessing.

ecommerce customer support

Connected next step

Continue into checkout assistance, a staff handoff or another supported action without discarding the discovery context.

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Product discovery and recommendation questions

It is a conversational product finder that asks about the shopper's need and uses available catalogue information to suggest relevant options. ZinQ connects that conversation to wider ecommerce workflows and handoff.

The answer depends on the catalogue, but useful fields can include product descriptions, variants, supported availability data, price, compatibility details and the business rules that shape a recommendation.

It can explain supported differences found in approved product information. It should not invent attributes or claim a product fits when the required data is missing.

The workflow can ask another useful question, say that no supported match is available or hand the conversation to a team member.

It can complement them. Some shoppers know the exact item they want; others need help translating a real-world need into catalogue options.

Test product discovery with your catalogue questions

See how ZinQ asks, narrows, explains and hands off using approved product information.

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