AI tools and agents put through real work rather than demos: hands-on results where we have them, and the test harnesses, benchmarks and failure checks that let a practitioner run the evaluation themselves.
Tools are run through real work for long enough to find what they fail at, and the failure is published beside the strength. A review that cannot name a weakness does not run, because this readership has been burned by demo-grade recommendations before.
Which is why a placement here converts. These readers are at the comparison stage, with a shortlist and a budget, actively looking for the write-up that says what a tool is bad at. Being honestly assessed here is worth more than being enthusiastically described anywhere else.
This is a comparison audience, which makes a placement here unusually measurable. Readers arrive with a shortlist and leave having removed something from it, and the article that helped is the one that named a weakness. Write-ups keep arriving from search for as long as the tool exists, so a placement goes on reaching buyers at the comparison stage well past the quarter it was bought in — which is the opposite of how advertising behaves. Readers also arrive from search at the exact comparison they are running, rather than from a feed they were scrolling.
People choosing AI tools for real work, in every kind of company: operations, marketing, finance, support, and the owners who sign for them. Explicitly a buying audience — they read comparisons because they are about to pick something.
Both are written for Tested at Work’s readers and reviewed by its editors before they run, and both stay on the domain permanently. What differs is how the page is labelled and how its links are treated.
Most startups & founders buyers start in the same two places: a question typed into a search box, and the same question put to an assistant. Neither returns a brochure. Both return whichever page answered the question properly — and for a company that launched eighteen months ago, that page almost always belongs to somebody else. An article in the section that covers the question is how you come to own one of your own.
Thornbury AI takes the position that this is a question of fit rather than feature count, and the piece argues that rather than asserting it. A placement is a permanent page on Tested at Work, written for its readers and reviewed by its editors, carrying up to three of the company’s own links with their own anchor text. It sits in the archive and the feed alongside everything else the desk publishes, and it goes on answering the question long after a campaign would have stopped running.
The trade-off is stated plainly in every piece we run: what agents that resolve support tickets end to end, with a human on the escalations is good for, and what it is not. That is not a concession, it is the reason the page is worth citing. A page that lists only strengths reads as advertising to a reader and to a model, and an article that reads as an advertisement is declined and refunded.
Every article publishes in English and Spanish, as two indexed pages on this domain, each with its own permanent URL. Both are written for the reader rather than translated around a keyword, and both are reviewed before they run.
2 indexed pages on testedatwork.com — one per language, each a permanent URL in the archive and the feed.
Your article runs in Agents, beside the newsroom’s own work on the same subject and in the same format.
The Tested at Work editors check the claims and decline anything that reads as advertising — refunded in full. That review is what makes a placement here worth citing.
Sponsored $99 or authored $149 for this title, both languages included.
One story, published across 20 independent publications in 24 industries — so search engines rank you and assistants name you.