Ecommerce automation that knows what not to touch
The jobs that are too repetitive to do well by hand and too consequential to get wrong.
Most ecommerce automation is sold as magic and delivered as a rule engine nobody trusts enough to switch on. The useful test is narrower: does this remove a job you are currently doing badly because there is too much of it?
Three qualify. Writing listing copy for a thousand products. Watching competitor prices on a marketplace all day. Noticing which customers have quietly stopped buying.
Where AI genuinely helps, and where it does not
A language model is good at turning a set of attributes into a marketplace-appropriate title and description, in volume, in a consistent voice. It is good at spotting that a description contradicts the product’s own specification.
It is not good at deciding what your stock level is, what something should cost, or whether a listing is compliant — those have right answers, and a plausible-sounding wrong one is worse than no answer. So generated copy is a draft you approve, and nothing generated is published to a marketplace on its own.
Repricing, with a floor you set
Marketplace pricing moves all day and the buy box goes to whoever is currently competitive. Repricing rules move your price within a band you define, with a floor that protects your margin, so the automation can win the position without giving away the reason for having it.
The rules are tiered rather than a single percentage, and they round to prices that look deliberate rather than computed.
Image compliance
Amazon rejects images for reasons that are precise, published, and tedious to check by hand across a catalogue: background not pure white, product too small in the frame, wrong aspect ratio. Checking those is arithmetic on pixels, which is exactly the kind of job to automate.
Editing them is not. An automated background removal that took a face off a photograph is a real thing that happened here, which is why the tool reports what is wrong and prepares a fix for review rather than silently rewriting your library.
Customers who drifted away
The most valuable segment in most catalogues is people who used to buy and stopped. They already know you and already converted once. Finding them is a query, not an insight — the work is running it consistently and following up, which is what automation is for.
The principle underneath all of it
Automation is allowed to prepare work and allowed to do reversible work. Anything irreversible — publishing to a marketplace, overwriting a listing, changing a stock figure — waits for a person, or passes through a rule that fails closed.
Automated repricing that respects your margin floor
Move your price to stay competitive, inside a band you set, with a floor the automation cannot cross.
Read more →AI product copy you approve before it goes anywhere
Draft descriptions for a whole catalogue in one pass, in your voice — and nothing is published until you say so.
Read more →Amazon image compliance, checked across the whole catalogue
Find every image Amazon will reject before Amazon rejects it — and be told which ones need reshooting rather than fixing.
Read more →Customer segments that describe behaviour, not demographics
Group people by what they have actually done — spent, bought, returned, stopped — and act on the groups rather than admiring them.
Read more →Winning back customers who quietly stopped buying
The people most likely to buy from you next are the ones who already have — and stopped without telling you.
Read more →Upsells at checkout and after it, without wrecking the order
Raise the average order by offering the obvious companion — and take no for an answer.
Read more →Every feature in this area
The rest of the platform
One catalogue, every channel
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