You've seen Open Graph tags without knowing it. Every time you paste a link and it unfurls into a neat card with a title, a description, and an image, that's Open Graph at work. The same quiet snapshot now shapes how AI previews and understands a business — and the interesting question for an agency isn't how to write the tags. It's why the assistant reaches for them in the first place.
Strip away the technical name and what you have is a short, structured summary the business wrote about itself: a headline, a one-line description, an address, and a link to a preview image. The text fields are the part that matters to an AI — they're plain words written into the page's code, which it reads directly; the image is for the card a human sees, not something the assistant interprets. It was originally built so social platforms could render a tidy preview. But it turned out to be useful far beyond that, because it answers a problem every AI system faces — how to describe a business in one clean breath without re-reading the entire website and guessing what matters.
Put yourself in the assistant's position. It is trying to be efficient and, more importantly, it is trying to be right. A full webpage is messy — navigation, promotions, boilerplate, a dozen competing messages. If the AI has to infer "what is this business, in a sentence?" from all of that, it might land on the wrong emphasis. It might call a specialist a generalist, or miss the city, or lead with a service the owner barely offers. Every time it guesses, it takes on a small risk of describing the business inaccurately — and an assistant that describes businesses inaccurately loses the user's trust.
So when there's a clean, structured summary already sitting there, the AI treats it as a gift. It's the business telling the assistant, in the business's own words, "here is who I am and what I do." That is lower-risk for the AI than improvising, which is exactly why it favors it. The machine isn't being lazy by using your summary — it's being careful. It would rather repeat a description you stand behind than author one it might get wrong and have to answer for.
The consequence is simple and a little unnerving: if the summary is blank or vague, the AI fills the gap itself, and now a machine is deciding how your client gets introduced. "Plumbing services" tells the assistant almost nothing it can match to a real request. "Licensed emergency plumber in Katy, serving the area since 2005" tells it exactly which questions this business answers. One of those gets pulled into a recommendation; the other gets passed over because the AI couldn't tell what it was looking at.
There's a second, subtler payoff. When the summary repeats the same core facts that appear in the visible content, the profile, and the structured data — same name, same city, same services — it adds another point of agreement. The AI is always cross-checking, and every place the story matches itself raises its confidence that the business is real and consistent. A summary that contradicts the rest of the site does the reverse: it plants a small doubt.
This is also why the summary outlives its original purpose. It was invented for social cards, but the assistant doesn't care what it was built for — it cares that the information is structured, compact, and self-described. As AI systems increasingly read the web to answer questions rather than just to index it, anything that hands them a clean, pre-digested description becomes disproportionately useful. The businesses that thought of this as a social-media afterthought are, almost by accident, the ones giving the assistant the easiest possible introduction to work with. The ones who left it blank are quietly making the machine do extra work, and the machine, given a choice, prefers not to.
This is where agency owners can reframe a dry technical item into something a client immediately gets. Don't sell "Open Graph tags." Sell control of the introduction. Ask the client: when an AI describes your business to a potential customer, do you want to have written that description, or do you want a machine improvising it from your homepage clutter? Framed that way, it stops being code and becomes a question of who controls the first impression — and no owner wants to outsource that to chance.
The deeper way to think about it is legibility. Making a business legible to AI is really about assembling one clean, machine-readable summary of who it is — and then making sure nothing elsewhere contradicts it. That's the same instinct behind an llms file generator, which builds a plain-language profile an assistant can read and points out what's missing from it. The format varies; the underlying job is identical — hand the AI a trustworthy summary instead of making it guess.
The good news is that this is one of the lowest-effort, highest-leverage items on the whole list. A specific, honest, consistent summary — the title and description written in plain text the assistant can read — gives it exactly what it wants, and gives your client the rare luxury of writing their own introduction. (A real storefront or logo image still earns its place: a compelling card makes the link look legitimate when it's shared, which earns the clicks and visits that later turn into the reviews and reputation the AI does read — even though the assistant reads the words rather than the picture itself.) In a world where the AI might sum up an entire business in a single line, the only real mistake is letting something other than the business write that line.
Our scan checks whether your Open Graph tags exist, whether they're specific, and whether they match the rest of your presence.