SabNode mirrors your Shopify, WooCommerce or Magento catalog into Meta Commerce Manager, keeps stock and price live, and exposes the SKU graph to the flow builder so any bot can recommend, add to cart and check out — without ever leaving WhatsApp.
Meta Commerce Manager is a competent product, but it was not built for a merchant who edits prices three times a day and runs flash sales every weekend. The default workflow is upload a CSV, wait for review, fix rejections, then realise half the SKUs are out of stock on the website but still showing in WhatsApp. Customers tap, add to cart, then bounce when the agent says "actually, that variant is sold out".
The second pain is variants and bundles. Meta's catalog data model is flat — one row per SKU. Real D2C catalogs have parents and children: a t-shirt with five sizes and three colours is fifteen rows in Meta, but it is one product in Shopify. Keeping those rows in lockstep manually is a part-time job.
The third pain is that the catalog is invisible to your bot. Even if the storefront is perfect, the chatbot cannot say "show me the in-stock blue medium" without a separate query layer. SabNode treats the catalog as a typed graph that flows, agents and AI Studio can all reach into.
SabNode connects to your source of truth — Shopify, WooCommerce, Magento, BigCommerce or a flat CSV in SabFiles — and continuously projects it into Meta Commerce Manager via the official Catalog API. Every 15 minutes we diff the source against Meta and push only the deltas: stock changes, price drops, new SKUs, deletions. Variant expansion is handled for you; one parent product in Shopify becomes the right set of Meta SKUs with the correct group_id and variant attributes.
Inside SabNode, your catalog is a queryable resource. The flow builder ships a "Product Search" node that takes natural language ("show me waterproof backpacks under 3000") or structured filters (collection, tag, price range, in-stock) and returns a typed list. The bot can render those as a WhatsApp interactive multi-product message, or hand them to AI Studio for an LLM-grounded recommendation.
Cart messages are first-class. When a customer taps "Add to cart" on a multi-product message, WhatsApp sends a structured cart payload that SabNode normalises into an Order draft. Your flow can quote shipping, apply a coupon, collect the address and then drop a payment link — all without redirecting to the website. The same cart payload writes to Shopify as a draft order so your operations team sees one source of truth.
For merchants in India, we handle the GSTIN, HSN and place-of-supply fields, and we surface DLT-template-safe product names for utility broadcasts. Catalog quality issues (rejected images, missing brand, blocked words) surface in the SabNode dashboard with a one-click fix, not buried four screens deep in Meta.
Capabilities
Connect Shopify, WooCommerce, Magento or BigCommerce by OAuth. SabNode polls or listens to webhooks, diffs against Meta and pushes only deltas. Manual CSV upload works too for legacy stores.
A single Shopify variant tree (size × colour × material) is auto-expanded to the right set of Meta SKUs with group_id, color, size, pattern and gender attributes filled in. Bundles and configurable products are preserved.
Inventory and price update within 15 minutes of the source change. Out-of-stock SKUs are hidden from interactive multi-product messages so customers never see a "sold out" surprise in chat.
Render up to 30 SKUs in a single interactive WhatsApp message. Sections, headers and call-to-action buttons are configurable. The flow builder treats the response as a typed Cart object.
When the customer taps Add to Cart, SabNode receives the cart payload, validates stock, applies coupons, and exposes a Cart variable to the flow. Quote shipping, collect address, fire a payment link without context switching.
A daily check flags Meta-rejected items, low-quality images, missing brand or banned keywords, and surfaces fixes. Merchants resolve catalog rejections in minutes instead of discovering them at scale.
AI Studio can ground LLM answers on the live catalog. Ask "recommend a gift under 2000 for a 30-year-old" and the bot replies with three in-stock SKUs, each backed by a real product URL and price.
Use cases
A fashion brand publishes a private flash collection only in WhatsApp. The catalog is filtered by tag "vip-drop" and rendered as a multi-product message to a 40k segment. Stock drains in real time, sold-out variants disappear automatically.
A gifting site uses AI Studio plus the catalog to take "recommend a 1500 rupee gift for a colleague who loves coffee" and reply with three SKUs as a multi-product message. The agent can edit the suggestion before sending.
A wholesaler exposes a price-list catalog gated by contact tag. Tier-2 buyers see one set of prices, tier-1 buyers see another. The agent quotes via cart message, draft order writes back to Magento.
A diagnostics chain models each test as a SKU with HSN code and place-of-supply. Patients pick tests via multi-product message, the cart converts to a booking with home-sample-collection date and a Razorpay payment.
A cloud kitchen syncs its POS menu nightly. Diners chat, browse a multi-product menu, drop items into a cart and pay via UPI. The cart hits the POS as a paid order and the kitchen ticket prints automatically.
How it works
Product Catalog is included on every SabNode workspace. No separate billing, no extra setup, flip it on from your workspace settings.
OAuth into Shopify, Woo or Magento. Pick the collections and tags to mirror. SabNode reads the variant tree and counts SKUs.
Authorize the WhatsApp Business catalog. SabNode creates or selects the target catalog and validates required fields.
First-time push uploads the catalog with variant expansion. Image and field validation runs before submitting to Meta.
Webhooks and a 15-minute fallback poll keep stock, price and metadata fresh. Diffs only — no full re-uploads.
Drop Product Search and Cart Message nodes anywhere. Bots and AI Studio query the catalog as a typed resource.
Connect directly with your existing stack or leverage the Platform Core tools to extend capabilities natively.
Enhance this feature with deep integrations into our core infrastructure. Connect via API, utilize webhooks, or embed directly using our SDKs.
Manage all settings seamlessly within the core UI.
Extend functionality with custom automated workflows.
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