If not, you are losing work to the window cleaners who figured this out first. This page is the 15-point checklist we run on every window cleaner website we audit. Start with the free checker, or skip to the $15 workbook.
When a customer asks ChatGPT, Perplexity, Claude or Google AI Overviews for a window cleaner, the tool answers directly. It names two or three businesses and the customer picks from that short list. Every window cleaner not on the list is invisible for that query.
The usual gaps on window cleaners websites:
None of that is hard to fix. Most of it is under an hour per item.
The first 40 words of the homepage, rewritten.
Bespoke exterior fenestration maintenance solutions across residential and commercial portfolios.
Window cleaners in Cardiff. Monthly residential rounds, gutter clears, conservatory roofs and solar panels. Pure-water reach-and-wash.
The after version is the one ChatGPT can match against “window cleaner near me monthly”. The before version is functionally invisible to AI search.
Same list for every business we audit, adapted to window cleaners as we go. Items are ordered by impact, not difficulty.
The first 40 words of your homepage do most of the work for AI search. If they read like a brochure, models cannot match them against real queries like “window cleaner near me monthly”. Rewrite them in the same plain English a customer would use when recommending you to a friend.
window cleaners websites over-index on trade jargon. AI models match semantic meaning, but only if the meaning is there in the first place. Swap insider terms for the words customers actually type.
FAQ schema is the cheapest-impact fix for window cleaners. Add six to eight real questions with plain-English answers, wrap them in FAQPage JSON-LD, and paste them into the head of your services page. Models pull directly from this block.
Get the schema type right or every downstream signal is weaker. Most window cleaners businesses belong under LocalBusiness (with the correct sub-type). Online-only operators sit under Organization. We give a decision tree below.
Google Business Profile is the single biggest off-site lever for window cleaners. Rewrite the description in 750 characters: what you do, where, and the three services people actually ask for, in customer language.
Set the primary category to the most specific correct option, and use every secondary category slot that fits. window cleaners accounts routinely leave half of the available category slots empty.
List the towns, postcodes or counties you serve as a plain text list and as a schema ‘areaServed’ array. Models use this to answer ‘near me’ queries.
Your business name, address and phone number must match across your website, Google Business Profile, Yell, Checkatrade and every directory. Any mismatch lowers the confidence AI models place in the data.
Models rank entities by the quality of their ‘knowsAbout’ signal. A good About page gives a clear who, how long, where, and why. Not a personal memoir, and not a list of awards.
Models pick up the language customers use about you. If your reviews mostly say “turns up the same week each month, leaves no streaks, fair price”, that is the signal. Prompt happy customers gently with the phrases you want to hear back. Real quotes only. The FTC is watching.
Once a month, paste the ten queries your customers actually ask into ChatGPT, Gemini and Perplexity. Log which businesses are named. If you are not on the list, note who is, and what they are doing right.
Pick the three window cleaners cited instead of you. Compare their homepage hero, FAQ, schema, and GBP categories against yours. The gap is the backlog.
A single homepage listing every service is weaker than one page per service. Split monthly residential window cleaning onto its own page with its own Service schema.
Run the decision: physical premises customers visit → LocalBusiness (with the correct child type, e.g. Plumber, Dentist). Service covers a geographic area but no customer visits → LocalBusiness with areaServed. Fully online, no physical location → Organization. Getting this wrong is common in window cleaners.
The full backlog fits in one weekend for most window cleaners. We run it as a 14-step, time-boxed sprint in the workbook so nothing slips. Ship, then measure for a month.
The basics, in the order an AI model reads them:
Every item on the checklist below folds into this same picture. Get the picture right and citations follow.
The free AI Visibility Checker gives you the exact prompts to paste into ChatGPT, Perplexity and Claude. No signup required to see the result.
When somebody types “window cleaner near me monthly” into ChatGPT, Perplexity or Claude, the model names two or three businesses directly. If your business is not one of them, the customer never clicks through to your site. Most small window cleaners websites were not built for this kind of summary-first answer.
The first 40 words of your homepage. Rewrite them in plain English that matches how real customers describe what they want. That is the single highest-impact change, and it takes under an hour.
Most of it can be DIY using the $15 workbook. The audit at $197 is for businesses that want a priority-ranked list of exactly which fixes to ship first, with the specific copy and schema blocks ready to paste in.
ChatGPT indexes new content roughly every 1 to 3 weeks. Perplexity and Google AI Overviews are usually faster. Expect to see citation changes within a month of deploying the fixes.
Yes. The methodology covers the United States, Canada, the United Kingdom, Ireland, Australia and New Zealand. AI models weigh the same signals in every English-speaking market.
Yes. The free AI Visibility Checker at getseoforai.com/checker.html gives you the exact prompts to paste into ChatGPT, Perplexity and Claude to see whether your business is being cited. Takes under 60 seconds, no signup.