The hiring manager and the candidate may both ask an assistant first. Its answer can only repeat what your pages state.

Two buyers and a third moment: who asks, and what each has to find

The hiring manager names the role, the industry and the city, and wants three things: evidence you have placed that role there before, how the fee works (a share of first-year salary on contingency, an hourly markup for temporary staff, a retained search), and how long it takes. With web search on, an AI assistant has only your pages to answer the role and the city from, and can only repeat a fee model it finds there.

The candidate asks whether you are real and whether they pay. If the employer pays and the candidate pays nothing, write that sentence. A candidate who cannot find it may assume the worst, and an assistant asked "is this agency legitimate" answers from what it can read: a street address, named recruiters, the registrations your state requires, reviews from people you placed.

The third moment is the surge: a plant manager at 6 a.m. who needs 20 people by Monday. That question is about capacity and speed: how many, how fast, which shifts, which certifications, who answers the phone now. A page that states typical fill times and the hours a person answers, in text, is the page that can get that call.

Niche beats the list. A page that names forty industries answers "CNC machinists in Grand Rapids" no better than a blank page. Name the roles and the cities you place most, each on its own page, and let the list go.

What a staffing agency site has to state before an assistant can name it

Both of them are asking an assistant that reads your HTML the way an AI crawler does; for ChatGPT, OpenAI's crawler documentation names ChatGPT-User as the fetcher that opens your page when someone asks about you. It does not know your fill rate from your reputation. It knows what the page states.

EmploymentAgency is a LocalBusiness subtype on schema.org, so the fields Google documents for local business markup apply: name, address, telephone, opening hours and geo, one set per office. Each specialty can be a Service node with serviceType and areaServed, and sameAs ties the agency on your site to the profiles you keep elsewhere. Google recommends JSON-LD and says to mark up only what a visitor can see.

The candidate's question and the hiring manager's question are answered by the same fields. An assistant asked whether an agency is legitimate reads the about page, the contact page and the reviews; an assistant asked who places controllers in Omaha reads the specialty pages. Both need the legal name, the address and the fee sentence in text, and neither can use a badge.

What our crawl found, and why the identity figures matter most here

For our audit of 9,483 small business websites, a crawler that ran no JavaScript and obeyed robots.txt visited the homepage, contact page and about page of sites in 40 trades and 90 metros between late August and early September 2026. The audit publishes no separate cut for staffing agencies; the figures describe the whole sample, and the method section says how it was drawn.

43.1% of sites were inconsistent about who and where they are across their own three pages; 36.9% published more than one phone number, and 34.1% had no postal address a machine could parse. For an agency that candidates check before replying, an address a machine cannot read is a poor answer to "is this real".

Most (61.5%) had no question-and-answer content, and 50.7% put a question in a heading without an answer under it that a machine could separate out. "Do you charge candidates?" as a heading over a paragraph about values is the staffing version.

Reviews were invisible to the crawler on 64.7% of sites, and rating markup appeared on 8.9%. 17.8% had no schema.org markup at all; of the 82% that did, most described the website rather than the business.

In the audit's probe, one buyer question per business put to Claude with web retrieval on named 14.9% of 9,240 businesses: a direction, not a rate. Of every name given, 99.4% were actual businesses and 0.6% were directories or aggregators. Google's first page for those questions was mostly aggregators; the assistant's answer was not.

What to fix first, in order

1. Legal name, one address and one phone number, in text on every page and in the markup.
2. Who pays, in one sentence, on the candidate page and on the employer page.
3. One page per specialty by role and city, with the fee model and the typical fill time.
4. EmploymentAgency in JSON-LD on every page, with the fields filled and a Service node per specialty, describing only what the page shows.
5. Reviews from clients and from placed candidates as text, with a first name, the month and the platform. Under Google's review snippet rules, ratings from other websites may not be aggregated, and a business that controls the reviews about itself gets no review stars, so no platform rating goes in the markup.
6. The questions both sides ask on the first call, as headings with a real answer under each.
7. Check what a crawler receives: the crawler access check for robots.txt, and the source of a specialty page with scripts off.

A staffing agency's place in an answer is not for sale, and no agency of our kind can fix one for you. What the list does is make both of your buyers' questions answerable from your own pages; a monthly panel of their questions tells you whether the answers now name you.

Sources: our audit of 9,483 small-business sites, with its method · Google, local business structured data · Google, introduction to structured data · Google, review snippet · schema.org, LocalBusiness · schema.org, Service · schema.org, sameAs · OpenAI, crawler documentation