WhatsApp Catalog and AI Product Discovery for Conversational Commerce

Customers describe needs differently from catalog navigation. They may ask for a gift under a budget, a durable option for daily use, or an alternative to an unavailable item. AI can interpret that language, but useful results depend on product attributes that represent material, compatibility, size, use case, audience, and constraints.

Keep product identifiers, variants, current price, inventory, media, delivery restrictions, and lifecycle state in a catalog service. The assistant should query that service rather than memorize exports. When a product is retired or a variant sells out, it should disappear from recommendations without waiting for a prompt update.

Start with what the customer has already said and ask the smallest question that meaningfully narrows the options. For some categories that is budget; for others it may be size, compatibility, delivery date, or intended use. Avoid a long questionnaire before showing any value.

Translate natural language into validated catalog filters and preserve uncertainty. If affordable could mean several price ranges, ask rather than assume. Use the customer's language for the explanation while retaining normalized values for search, analytics, and subsequent cart actions.

Return a small, diverse set of relevant choices and state why each matches the expressed preferences. Distinguish verified attributes from general descriptions. The assistant should not claim a product is best, safer, compatible, or suitable for a regulated use unless approved data supports that statement.

Offer comparison on the dimensions that matter to the customer rather than repeating marketing copy. If no item satisfies all requirements, explain the conflict and ask which constraint is flexible. Honest partial matches build more trust than confidently forcing a product into every request.

When the customer chooses an item, resolve the exact variant and quantity, then recheck price, stock, delivery eligibility, and relevant policy. Store stable identifiers in the cart rather than relying on product names from the transcript. Summarize the selection before creating an order or payment request.

Use idempotent order creation and verify financial state through the payment provider or ledger. A generated link, QR image, or customer screenshot does not prove settlement. Confirm the purchase only when the order and payment systems return a definite result, and surface uncertain cases to operations.

Review zero-result searches, repeated refinements, misunderstood attributes, unsupported comparisons, unavailable selections, and frequent human interventions. These signals often expose incomplete catalog data or assortment gaps. Improving one important field can help more customers than rewriting the entire assistant persona.

Track discovery success through meaningful outcomes such as a useful shortlist, saved cart, qualified handoff, or completed purchase. Keep model and prompt versions attached to recommendation logs without storing unnecessary personal content. This makes changes testable while respecting customer data boundaries.