Why local sites need to be built for AI search, not just Google

Someone in your town asks ChatGPT or Perplexity where to eat tonight, or what happened at the last council meeting. The answer comes back quoting a big aggregator or a national directory. You covered that restaurant. You sat through that meeting. Your site was the one with the facts, and the assistant never mentioned it.

larkfeed builds local sites so AI assistants can understand and cite them. It is an AI-native platform for local media that puts a town's news, businesses, events and jobs on one site, built to be found and cited by Google, ChatGPT and AI search. The difference from an ordinary site is structural, not cosmetic, and it shows up in what the machines can read.

What larkfeed actually does

Every business, article, author and place on a larkfeed site is marked up with structured data. Structured data is a standardized way of labeling page content, a set of machine-readable tags that tells a search engine or an AI assistant exactly what it is looking at: this is a restaurant, these are its hours, this is the author, this is the date the story ran. When an AI tool answers a local question, it quotes sources it can parse and verify. Markup is how a page becomes one of those sources.

Each site also ships with a guide for AI crawlers, so AI tools can find their way around the content. And every article carries sources, dates and authors, which is what an assistant needs before it will attribute a claim to a publication. A story that says who wrote it, when it ran and where the facts came from is citable. A story that says none of those things is not, no matter how good the reporting was.

The markup covers the directory as well as the news. Every local business gets a real page with photos, reviews, hours and a map, and each page is structured so Google and AI assistants understand it. larkfeed loads local businesses into the publisher's directory for them, so the structured pages exist from day one rather than trickling in as owners sign up.

Who it is for, and who it is not for

larkfeed is built for local media companies and entrepreneurs in any market. That includes newsletter publishers who want a real website business, and operators starting a local media brand. The team behind it runs more than 20 local publications themselves and built the software for their own use first, which is why the assumptions fit operators rather than developers.

It is not a tool for a publisher who only wants a print-era website that ranks on Google and nothing else. The whole point of the platform is that AI assistants now answer local questions, and a site built only for classic search is invisible to them. It is also not a hands-off system. Nothing goes live until the publisher approves it, and larkfeed counts zero articles published without a human yes as a feature, not a limitation.

Why fresh local facts beat a generic directory

An AI assistant asked about a town has to choose between a generic national directory and a local site that covers the place every week. The generic directory has a business listing. The local site has the listing, plus a story about the council vote, plus the permit filing for the new storefront, plus an interview with the owner. When a machine can parse all of it, the fresher, more specific source is the one worth quoting.

larkfeed keeps those facts coming. It watches the meetings and filings a publisher picks, such as council agendas, meeting videos and building permits, and writes a sourced draft that waits for approval. Free event and job submissions keep the calendar and job board full. The AI article writer turns a brain dump of notes, photos and links into a designed, sourced draft, and a second AI edits and scores it. Every piece of that output lands with the dates, sources and markup an assistant needs.

A publisher's own stories, events and jobs also flow into their newsletter through lightbreak, larkfeed's sister product for newsletters, and every form on the site runs on greetform. The local site, the newsletter and the directory reinforce each other as one citable body of work rather than scattered pages on separate tools that never talked to each other.

How to see it