AI Automations for Ecommerce and DTC Operations

Support volume on a store is not a flat line. It has a mountain in it.

Say your queue runs at 200 tickets a day and peak takes it to 900. You are 700 a day short for five weeks, and overstaffed again in January. You cannot hire for that, and the seasonal people you do hire spend their first fortnight learning a returns policy they will never use again. That shape is what makes automation worth buying here. It is also what makes it hard, because something sized for February collapses in November.

The work that only exists for six weeks

The candidates on a store are specific and they repeat.

• Where is my order. A lookup, currently done by a person with three tabs open.
• Return authorisation. Checking an order against a written policy, which is a rule before it is ever a model.
• Address correction before the label prints. Cheap inside that window and expensive outside it, because afterwards it is a reshipment.
• Marketplace order exceptions. Orders that failed to import, mismatched SKUs, cancellations that never propagated back.
• Supplier chasing. Purchase orders with no acknowledgement, inbound shipments with no advance notice against them.

None of it is impressive in a demo. All of it is somebody’s afternoon.

Three systems that disagree about one order

What an agent is doing when they answer a shipping question is reconciling three systems. The storefront says the order is fulfilled. The warehouse says one line went to backorder. The carrier has a label created and no scan for four days.

An automation reading one of the three and answering confidently is worse than no automation. It produces wrong answers at machine speed, with your brand on them. So the first thing we build on a commerce stack is the reconciliation, not the reply. Once one view of an order exists and the disagreements are visible, the reply is the easy half.

Where the gate goes when the customer sees the result

We split actions by how expensive they are to reverse. On a store that split lands differently than inside a back office. The result is visible to somebody outside your company within seconds.

Tagging a ticket, routing it, drafting a reply a person sends, flagging an order for review. Cheap, reversible, unattended.

Money out, inventory out, or a price on a live storefront. Gated, every time. A refund issued in error is a conversation with a customer who has already had the email. A cancellation on an order that is already picked is a physical problem in a building. A price written live is visible to every buyer and every competitor before anyone has looked at it.

That last one has a tail worth naming. If you sell through dealers or resellers as well as direct, the risk is an automated repricer following a marketplace downwards. It can put your own advertised price under the floor you are asking your channel to hold. That is an advertised price enforcement problem and it belongs with the people who run that programme, not with whoever built the support workflow.

The automation we argue against

Generating product descriptions for the whole catalogue in one pass.

It is the first idea in most of these meetings and it is the one we push back on. Thousands of pages of generated copy land on the same domain as the collection pages earning your revenue. Google’s spam policies name scaled content abuse, and we treat it as a risk to the site, not only to the page. A catalogue is exactly the shape that gets caught, because the pages are near-identical by construction before anybody generates anything.

What we build instead is a queue. Drafts produced for the SKUs a person was going to review anyway, source data attached, nothing published until somebody presses a button. Slower and smaller, and it does not gamble the pages that pay for the building.

There is a second no. If your fraud rule is written down and has twelve conditions in it, it should be twelve conditions in code. A model in that slot is slower, costs money on every order, and cannot be explained to a chargeback provider.