Mirasvit - Magento Extensions

A relevant product recommendation can still be a weak shopping answer. If a chatbot shows only the item name, the customer has to leave the conversation to check the image, discount, available options, and final offer.

That gap is easy to miss because the reply may look correct at first. The product matches the request, the category is right, and the budget seems close. But in a real store journey, people do not decide from a title alone.

A product card gives them the signals they need before they click: what the item looks like, whether there is a special offer, how the cost changes across variants, and whether the visible range matches what they expected. When chat removes those signals, it can make the buying path feel faster at first but less useful in practice.

This is especially important for configurable, grouped, and bundle products. A clean number in a chat reply may hide a cheaper variant, a “from/to” range, or a crossed-out regular amount next to the sale offer. The item can be relevant, while the commercial context still feels incomplete.

A better store assistant keeps enough buying context inside the conversation. It should help the customer understand the option clearly enough to decide whether to open it, compare it with another item, or move closer to purchase.

In our eChat extension for Magento 2, the chatbot can show product images, sale prices, price ranges, and strikethrough regular prices where relevant, so recommendations feel closer to the catalog experience customers already understand instead of a plain text suggestion.

Learn more: https://mirasvit.com/magento-2-gpt-chatbot.html

A lot of e-commerce work slows down not when something happens in the store, but right after it. The order is placed, the shopper shows intent, the catalog changes, and then the next step still has to be pushed through by hand. That is the common thread across our June module updates.

▶️ Less rebuilding after checkout when orders need to be merged, split, or corrected
▶️ Email automation that reacts closer to what shoppers actually did
▶️ Product feeds that move into Google Merchant Center and ChatGPT with fewer manual handoffs
▶️ Search that stays easier to control across storefronts and catalogs

And those four areas are only part of the release. The full roundup also covers practical changes across AI workflows, storefront tools, merchandising, content, and reporting.
You can find all of that in our blog post, with examples and setup notes throughout: https://mirasvit.com/blog/mirasvit-latest-updates-june-2026.html

We’re wrapping up our series on practical AI Agent Integration (MCP) use cases, from simple prompts to more advanced workflows. Today’s example takes that one step further and shows how store data can flow into the tools people already use every day.

📌 Final case: turn a morning store check into a Slack update.
A familiar routine: someone reviews overnight orders, looks for unusual changes, checks stock warnings or failed payments, and then writes a short update for colleagues. The work matters, but it usually means switching between several screens and repeating the same information in Slack.

With MCP, that process can be much simpler. If your AI assistant is connected to both Magento and Slack, you can ask for a morning summary based on live store data and have it posted to the right channel.

Try a prompt like this: “Check last night’s orders, flag anything unusual, and post a short summary to our e-commerce ops Slack channel.”

Instead of pulling the numbers by hand and rewriting them for others, you get one connected workflow: the assistant reviews the data, highlights what needs attention, and sends the summary where it will actually be seen.

Slack is just one example. The same setup can also work with Google Sheets, Notion, Gmail, Google Docs, and other tools your team already uses.

Learn more: https://mirasvit.com/magento-2-mcp-ai-integration.html