The craft of AI features people trust: interface patterns that set expectations, pricing and launch sequences that survive a bad day, evals that catch regressions, and guardrails drawn from shipped products.
Patterns are drawn from products that actually shipped, each with the failure mode it was built to handle. An eval is shown as the thing it catches, and a guardrail is described by what it stops and what it costs to add.
Readers are mid-build, with a roadmap still open and the tooling still unchosen — model providers, eval platforms, analytics, design systems. This is the last moment before those decisions harden, and much the cheapest moment to be part of them.
Placements here land in the middle of a build, which is where they are worth most. An article that shows a pattern working — what it prevents, what it costs, what it looks like in an interface people already use — gets adopted rather than admired, and the adoption brings the tooling behind it. The pieces stay useful as long as the pattern does, which in this field is considerably longer than any individual model release. It is also a forgiving place to be honest. A piece that says what a tool will not do, and shows the case where the pattern breaks, is taken more seriously here than one that does not — this readership has shipped enough AI features to know that everything has an edge somewhere.
The people designing and shipping AI features — product, design and engineering — plus the founders deciding what their AI should feel like. Most of them are reading while a roadmap is still open and the tools are still being chosen.
Both are written for Prompt & Product’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 Prompt & Product, 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 promptandproduct.com — one per language, each a permanent URL in the archive and the feed.
Your article runs in Evals, beside the newsroom’s own work on the same subject and in the same format.
The Prompt & Product 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.