Inventory Checks and Product Recommendations with a Chatbot

What should be ready before setup?

You need access to the store platform, product catalog and stock data. When connecting a system such as Shopify, iKAS or T-Soft, define the fields and permissions in scope. Consistent product codes, variants, warehouses and quantities make matching easier.

Prepare management access for the website, WhatsApp or Instagram channel where the assistant will operate. Connecting a messaging channel does not automatically provide access to inventory.

How do you build the workflow?

1. Verify the store connection

Check that the integration can read the required product and stock information. Identify supported fields and action boundaries. The integration options are a starting point for that assessment.

2. Define the stock source and refresh interval

Decide where names, SKUs, variants and warehouse quantities come from. Whether updates are live or periodic affects what the assistant can tell a customer.

Note: Do not present periodically updated information as guaranteed live stock. More frequent queries may reduce delay, but the connection and failure scenarios still need testing.

3. Organize the catalog

Prepare consistent product names, categories, prices and images. Match sizes and colors correctly. Inconsistent category labels can lead to irrelevant alternatives.

4. Separate stock and order questions

Whether a size is available is a product-data question. Where a customer's order is depends on the order record. These workflows use different information; configuring one does not mean the other is ready.

5. Define recommendation rules

Category, price range and permitted customer information can shape recommendations. An alternative should be relevant and available. When no suitable match exists, an explanation or agent handover is better than an unrelated suggestion.

6. Test the channels

Use products that are available, sold out and offered in several variants. Ask the same question across channels and check that the same source data is used. Also test what happens when a record cannot be found or the source system does not respond.

7. Monitor results

Track answer accuracy, response time and actions following recommendations. Revisit rules that perform poorly or product matches that repeatedly cause confusion. Assess the outcome using the store's own data.

How do you know the setup works?

• Stock replies agree with the source, allowing for the defined refresh interval.
• Variants such as size and color match correctly.
• Suggested alternatives fit the customer's need and configured rules.
• Channels use the same information source.
• Missing data and connection errors lead to an explanation or handover, not a guess.

Common mistakes

Hiding data-freshness limits, leaving category labels inconsistent, making recommendation rules too narrow and skipping tests are common problems. Another is treating order tracking and product availability as one operation. Validate each with the information and permissions it needs.

Where does human support fit?

Standard stock and product questions suit automation, while unusual needs and uncertain matches may require an agent. Palmate's shared Manuka example reports a 40% improvement in support efficiency. That customer result does not mean every inventory project will achieve the same improvement.

Explore the Palmate e-commerce chatbot approach and review usage plans. Book a demo to assess a workflow using your catalog.