Industry
AI automation for ecommerce brands whose team spends the day answering order questions, fixing catalog data and chasing exceptions.
Sound familiar?
Most ecommerce teams we talk to are not short on tools. They have Shopify or WooCommerce, a helpdesk like Gorgias or Zendesk, Klaviyo, a 3PL portal and a couple of spreadsheets that hold everything together. The problem is the work that happens between those tools: copying tracking numbers into replies, retyping supplier price lists, writing the same product copy for the hundredth time, checking a return against an order history before approving it.
That is the work AI automation for ecommerce is good at. A customer support agent can answer order-status, shipping and returns questions from live order data and hand anything unusual to a person with the context already attached. Supplier files can flow into your catalog on a schedule instead of through someone's afternoon. Our own Shopify app FeedGuard does exactly this for CSV, FTP and Google Sheets feeds, and we build custom versions when your suppliers are messier than a standard feed allows.
We also build the less visible pieces: daily stock and margin reports that land in Slack, enrichment jobs that draft product descriptions for a merchandiser to approve, and Shopify automations that tag, route and hold orders based on your rules. For refund abuse specifically, Return Intelligence scores return requests with transparent rules, so your team sees why a request was flagged rather than trusting a black box.
We start small. One workflow, measured against how long it takes today, live in a few weeks. If it pays for itself, we do the next one.
The problem
If you track a week of work in a growing online store, a pattern shows up quickly. Very little of the manual effort is hard. It is repetitive, it touches three or four systems, and it has to be done today.
In many stores, the majority of tickets are "where is my order", "can I change my address", "how do I return this" and "is this back in stock". Each one takes a minute or two: open the helpdesk, find the order in Shopify, check the carrier page, paste a reply. At 150 tickets a day that is a full-time person doing lookups, and it gets worse every sale and holiday season.
New products need titles, descriptions, tags, alt text, metafields and collection placement. Supplier price and stock changes arrive as spreadsheets, PDFs or emails in whatever format the supplier likes. Someone reconciles them by hand, and mistakes show up as overselling or wrong prices.
Orders stuck in fulfillment, failed payments, address issues, split shipments, high-risk returns. These usually live in someone's head or a shared inbox, and they get handled when someone notices.
Weekly numbers often mean exporting from Shopify, the ad platforms and the 3PL, then building the same spreadsheet again. By the time it is done, it is out of date.
None of this needs a new platform. It needs the systems you already pay for to talk to each other, with an AI model doing the reading and writing where rules alone fall short, and a person approving anything that costs money or touches a customer in an unusual way.
Not everything should be automated. Replies to a customer who has had a genuinely bad experience, decisions on large or unusual refunds, and creative work like campaign concepts and photography direction are better handled by people. We would rather automate three boring workflows well than put an AI in front of a conversation that needs empathy and discretion.
Where we start
An AI agent in Gorgias, Zendesk or your site chat answers tracking, shipping-time and return-policy questions using live order and carrier data. It escalates refunds over a threshold, angry customers and anything it is unsure about, with the order summary attached.
Price and stock files from suppliers are fetched, cleaned and mapped to your SKUs on a schedule. Unmatched rows and big price jumps are held for review instead of going live.
New products get draft descriptions, SEO titles, alt text and tags written from supplier specs and your brand guidelines. A merchandiser approves or edits them in a queue before they publish.
Orders stuck in fulfillment, with mismatched addresses or with fraud signals are flagged in Slack with a suggested action, so they are handled the same day instead of when a customer chases.
Sales, margin, stock cover and ad spend pulled into one short summary posted to Slack or email every morning, with a plain-English note on what changed and what looks off.
Sell-through rates are compared with stock on hand and supplier lead times, so buyers get a reorder warning before a bestseller runs out, and waiting customers get a back-in-stock email the moment inventory lands.
Integrations
Orders, products, customers, metafields and webhooks through the official Admin and REST APIs, including Shopify Flow where it already does the job.
Gorgias, Zendesk, Intercom, Re:amaze and Help Scout, so the agent works inside the inbox your team already uses.
ShipStation, ShipBob, ShipHero and carrier tracking APIs for live status, delays and delivery exceptions.
Klaviyo, Meta and Google Ads, Judge.me, Yotpo and Okendo for reporting and for routing review feedback to the right person.
Cin7, Linnworks, Brightpearl, Xero and QuickBooks for stock levels, purchase orders and reconciliations.
Google Sheets, Airtable, FTP and SFTP for the supplier files and team trackers that never quite go away.
FAQ
It can if it is allowed to guess, so we don't let it. The agent answers only from your order data, policies and help content, and hands off to a person when the answer isn't there. We test it against a few hundred of your real past tickets before it goes live.
No. We build around what you already run, whether that is Shopify with Gorgias or WooCommerce with Zendesk. Changing platforms is rarely the fastest way to save time.
We use API access scoped to what each workflow needs, and model providers' business APIs that do not train on your data. Personal data stays in your systems where possible, and we can redact fields before anything is sent to a model. You remain responsible for your privacy policy, and we will tell you what data each automation touches so you can update it.
Running costs are usually model usage plus any automation platform fees, and for most stores they are small next to the staff time saved. We estimate monthly running costs during scoping, before you commit to a build.
Usually yes. We work alongside the apps you have and only replace one when it is clearly the bottleneck. Where one of our own apps, such as FeedGuard or Return Intelligence, already fits, we'll say so rather than build the same thing again.
Tell us what your team does by hand every day. We'll suggest where to start, what it would take and what it would likely cost to run, before any build begins. Most first workflows go live in a few weeks, and you can judge the results in your own helpdesk and store data rather than in a slide deck.
Each solution is built from one or more of these. Follow a link for the detail on scope, stack and process.
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