Ecommerce teams are small for the amount of data they sit on. Sales are in the back office, traffic in Google Analytics, ad spend in Google Ads, margins in the accounting software, and the questions that matter ("which category lost margin after the price change?") need someone to pull all of it together. We build AI agents that do that pulling, check your catalog at scale and triage routine work, while your team keeps the decisions.
How can AI improve ecommerce operations?
AI improves ecommerce operations by answering data questions across systems in seconds, checking and improving product content at catalog scale, and handling the first pass of customer requests. The gains come from connecting the AI to your own data, not from a generic chatbot.
Three agents we build for ecommerce teams:
- A conversational data analyst. Ask "how did repeat customers behave on the spring campaign?" and get an answer computed from your back office, analytics and accounting data, with the query behind it.
- A catalog and compliance checker. Review product pages at scale for missing information, inconsistent attributes and risky claims, and propose corrections.
- A customer request triage agent. Classify incoming emails and messages, answer the routine ones from your policies and order data, and route the rest to the right person with a summary.
A conversational data analyst, without another dashboard
Most stores already have dashboards nobody opens. A conversational analyst answers the question you actually have, in plain English, and shows how it got the answer. The architecture we propose:
| Layer | What it does |
|---|---|
| Nightly sync | Copies sales, product, customer, analytics, ads and accounting data into a single database |
| Business views | SQL views that define your metrics once (margin, repeat rate, contribution by channel) and apply access rights and anonymization |
| AI connector | An MCP server that exposes those views to the assistant, read-only |
| Assistant | Claude, ChatGPT or Gemini, whichever your team already uses |
The assistant never queries your live store directly, never sees data a user is not allowed to see, and cannot change anything. Defining metrics once in the business views is what keeps its answers consistent with your finance team's. The connector layer is the same we use in our AI integration services.
Built and in production: product-page review at scale
Ficheck, one of our own products, reads a product page from its URL and flags claims that create regulatory risk, for food supplements, cosmetics and food sold in France. For each flagged claim it shows the regulatory source and proposes compliant replacement wording; it never decides for you. With a subscription, pages are re-checked automatically when the page or the rule changes.
The rules it applies are French and European, so it is not a fit for a US-only catalog as is. The engineering is what carries over: a small model spots candidate claims, a stronger model judges them, another drafts the fix, and costs are capped per analysis. The same pattern checks a US catalog against your own content rules or your marketplace's listing policies.
Which AI is best for ecommerce?
The best AI for ecommerce is the one connected to your own data: a general assistant such as Claude, ChatGPT or Gemini becomes far more useful once it can read your orders, catalog and analytics through a secure connector. Built-in AI features of your platform are a good start for content; custom agents fit when the work spans several systems.
What stays with your team
| The agent does | Your team does |
|---|---|
| Pulls and computes data across systems | Decides on pricing, promotions and spend |
| Flags product pages and drafts corrections | Approves and publishes the changes |
| Drafts replies to routine requests | Handles refunds, disputes and exceptions |
| Reports anomalies (sudden drop in a channel, stock-outs) | Investigates and acts |
How a project runs
- A 30-minute call to pick the first use case.
- A data audit: which sources, how clean, how they reconcile with each other.
- A written scope and a fixed quote, then a first version on your real data.
- Launch with monitoring and a monthly plan to keep the connector and the metrics up to date.
For agents in general, see AI agent development.