---
title: "How the AI searches products"
description: "Understand how Interlinked prepares product catalogs for AI search, what semantic product search means, and why processing time can vary."
section: "Products"
url: https://interlinked-ai.com/en/resources/docs/how-ai-searches-products
lang: en
lastUpdated: 2026-04-24
reviewedAt: 2026-04-24
---

# How the AI searches products

## Direct answer

After products are uploaded, Interlinked prepares them for AI search. This process helps the agent find products by meaning, not only by exact keywords.

Products appear in the table immediately after upload or changes. AI search preparation may take a few minutes depending on catalog size and detail level.

## What AI search preparation means

Interlinked reads the product information you provide, including names, prices, stock, descriptions, categories, and attributes. It then prepares that information so the AI agent can retrieve relevant products during customer conversations.

You do not need to understand the technical details. The practical effect is that the AI can search across product meaning and context, not only exact text matches.

## Examples of semantic product search

Semantic product search helps the AI interpret customer questions such as:

| Customer asks | What the AI can use |
|---------------|---------------------|
| "What is your largest protein presentation?" | Size, weight, presentation, product name, and description |
| "What is the most affordable option?" | Product prices |
| "Do you have something for sensitive skin?" | Product descriptions, categories, skin type, and attributes |
| "I need a black formal shirt in medium." | Product name, category, color, size, and description |

The more complete your catalog is, the better your AI agent can answer product questions. A short description, product category, size, color, flavor, material, or use-case column can help the agent understand what each product is and when it should recommend it.

## Timing expectations

Products appear in the Products table immediately after upload, replacement, manual addition, or clearing. AI search preparation is separate from table visibility.

For most catalogs, AI search preparation should complete within a few minutes. Very large catalogs or catalogs with extensive details can take longer. If the provided product count is very high, for example tens of thousands or more, embedding and search preparation can take longer.

As a practical expectation, allow a few minutes before testing every product with the AI agent after a large upload or replacement.

<div class="docs-callout docs-callout-note">
  <p><strong>Note</strong></p>
  <p>The AI agent may reflect catalog changes quickly, but do not rely on every semantic search result being fully ready the exact second a large upload finishes.</p>
</div>

## Why descriptions and attributes matter

Product names alone are often not enough for natural customer questions. A customer may describe a need, constraint, use case, material, color, flavor, or compatibility requirement without using the exact product name.

Descriptions and attributes give the AI more context to identify relevant matches and explain them clearly. For best results, include concise, factual product descriptions and structured attributes in your catalog.

## What to do if a product is hard to find

If the AI does not find a product as expected:

- Confirm the product appears in the Products table.
- Search the table by name, SKU, and key attributes.
- Check that price and stock were imported correctly.
- Add a clearer description or category.
- Allow a few minutes after large uploads before testing again.

For data cleanup guidance, see [Product catalog best practices](/en/resources/docs/product-catalog-best-practices).