A check that catches the mistake in a description before the customer does Hundreds of descriptions, articles, or replies go live every month, and checking each one by hand is unworkable at that pace. A mistake — a typo in the price, an outdated spec, a tone that doesn't match the brand — only surfaces once a customer reports it.
Ready to publish without client data. --- # A check that catches the mistake in a description before the customer does
Hundreds of descriptions, articles, or replies go live every month, and checking each one by hand is unworkable at that pace. A mistake — a typo in the price, an outdated spec, a tone that doesn't match the brand — only surfaces once a customer reports it.
The more content a company publishes, the harder it is to keep every piece consistent with the facts, the brand's tone, and up-to-date data. An editor checks what they have time for, and the rest goes live on trust in the author.
Mistakes that slip through — a wrong figure, a tone that doesn't fit the rest of the catalog, wording that breaks a policy — cost more after publishing than before, because they get fixed under the pressure of a customer complaint.
We build a review layer that checks every text before it goes live against defined rules — consistency with source data, brand tone, banned phrasing — and flags text that fails those rules. Nothing goes live automatically against the result of that check.
We define the review rules together with you based on mistakes that have actually happened, not a generic list off the internet. The system compares content against source data, so it catches a mismatched number or name, not just language errors.
What we don't do: we don't automate the decision to publish a text that fails review — it goes to an editor with a specific note on what raised a concern, and a human decides whether to fix it or make an exception.
This is a new service line — we don't yet have published deployments to show off, and we say so directly rather than inventing references. We run first deployments on preferential terms, in exchange for the right to describe the result, without client data, as a reference case.
The scope covers one type of content and one set of review rules — for example, checking product descriptions against catalog data.
Price depends on the number of review rules, monthly content volume, and whether integration with your content system is needed. You know the amount after gathering the rules, not before it.
A human in the loop. Text that fails review goes to an editor — the system never decides on its own to publish or reject.
You set the success threshold. Before we start, we agree on what share of known mistakes the system must catch for the rollout to make sense.
A decision log for auditing. Every flag and every editor decision is recorded in a history you have ongoing access to.
The same team that builds content quality control systems also runs the rollouts for our other automation services — so the number of new projects we take on each month is limited.
Send us examples of mistakes that have happened in your content so far and your publishing volume — we'll reply within a few business days with the scope of work and a firm price.
Every month without this check is a month where mistakes in your content surface only after a customer flags them, not before publishing.
A check that reviews content against agreed rules before it goes live · non-compliant text goes to an editor, not straight to the bin or straight to the site · tested on past content before rollout · success threshold agreed before we start · a full decision log for auditing.
No — content that doesn't meet the rules goes to an editor, who makes the final call.
We define the rules together with you, based on mistakes that have already happened in your content, not a generic list.
It compares content against source data, so it also catches mismatched numbers, names, or specs, not just typos.
Yes, rules can be extended as new types of mistakes appear in your content.
The check runs in the background during content preparation, so it doesn't noticeably slow down publishing.
Yes, as long as it has access to the content before publishing — it works regardless of who or what authored the text.