Is the audit really free?

Yes, for the requests we take on. We run it as the entry point to a working relationship, not as a lead magnet with a paywall at the end: you get the full scorecard, the engine-by-engine verdicts and the dated 90-day plan, and you keep all of it whether or not you hire us. What we ask in return is a real conversation about the findings. We are referral-first and capacity-bound, so we do not take every request.

How long does it take?

Between five and ten working days from the moment we have access, depending on the size of the site and how many markets you want covered. It is manual work by a small team, not a tool run, so we schedule a limited number at a time.

What do you need from us to start?

Your website and your top three priority geos. That is the whole ask. Everything else the audit needs it takes from the public web the way an engine sees it — your pages, your competitors, and what the models already answer about all of you. No access, no exports, no homework.

How is this different from an SEO audit?

An SEO audit asks whether Google can rank you. This asks whether six AI engines can read you, verify you, and quote you when a buyer asks who to pick. The two overlap on the technical layer and diverge completely after it: entity consistency, citation sources, the prompt panel, and the verdicts models currently give about your brand have no equivalent in a classic SEO report. We cover the SEO layer too, because many of the sources engines cite are themselves ranked pages.

Do you use a tool for the AI visibility part?

No. Every prompt in the panel is run live against each engine and the answer is logged verbatim, with the sources it cited. Tools that poll an API give you a number without the answer behind it; we want the sentence the model actually produced about you, because that sentence is what your buyer reads.

What if the audit says we are fine?

Then we say so and you keep the scorecard. It has not happened yet in this category: across the 100 audits in our published dataset, not one site cleared 60 out of 100, and the average was 35. But the report is a measurement, not a sales script, and it goes out with whatever number the measurement produced.

GEO audit - what six AI engines answer about you

A GEO audit is a manual measurement of how AI engines see, describe and recommend your brand. We run a fixed panel of your buyers’ real prompts through ChatGPT, Claude, Gemini, Perplexity, Grok and DeepSeek, log every answer verbatim with its sources, check what the crawlers behind those engines can actually read on your site, benchmark the competitors the models name instead of you, and hand back a health score out of 100 with a dated 90-day plan.

It is the entry point to everything else we do, and it is complimentary for the requests we take on. You keep the scorecard and the plan whether or not we work together.

why this is a different exam

Your buyer used to open Google, skim ten links and form a view. Now they ask an assistant “who should I pick for X” and get a shortlist of three. If your name is not in that shortlist, the rest of your marketing never gets a hearing — not because you ranked eleventh, but because the model had no reason to name you.

That is a different failure from a ranking failure, and the numbers say so. Across the 100 audits in our published dataset (n = 100, April–July 2026), 93% of brands were known to AI by name and described accurately — and only 4.5% were named when a buyer asked who to pick. Being known and being recommended turned out to be almost unrelated. We found brands with excellent organic traffic and zero presence in AI answers, and brands with almost no traffic that engines quoted first.

So a classic SEO audit will not find this. It measures whether Google can rank you. This measures whether a model can read you, verify you, and quote you.

what you get

the seven sections of the report
no.	section	what is inside
01	where you stand today	Health Score out of 100 and the numbers behind it: ranked keywords, traffic value, page speed, schema coverage, llms.txt, crawler access.
02	whether AI names you	Your buyers’ real prompts run live across six engines: who gets named, who gets cited, from which sources, how often you appear — and the prompts where you never do.
03	who it names instead	Every competitor the models named, benchmarked on the same scale: traffic, citations, entity coverage, their strongest pages, and where they are thin.
04	what is blocking you	Three critical blockers with severity and the evidence behind each, plus the second-tier list you can hand to a developer.
05	what to publish	The keyword and prompt map, and the exact pages to ship first, with volumes, difficulty and the intent behind them.
06	the plan and the targets	Sprint deliverables with exit criteria and a target figure for every KPI we would be judged on.
07	the hand-offs	Ready-to-apply artefacts, not homework: llms.txt, JSON-LD blocks, redirect and header configs, page briefs.

how we run it

Ten specialist passes, each one a person looking at a different failure mode, then a synthesis that ranks everything by what it costs you. The order is not cosmetic — each pass depends on what the previous one found.

Recon. What the domain is, what it was before, and what the open web already says about it. Domain history matters more than people expect: we have seen a previous owner’s reputation still driving the answer a model gives today.
Technical. Redirects, canonicals, duplicate hosts, security headers, sitemap and robots hygiene — the layer that decides whether anything else is even reachable.
Crawler reality check. We request your pages as GPTBot, ClaudeBot, PerplexityBot and the rest, and compare what comes back with what a browser sees. This is where client-side rendering quietly deletes brands from the corpus.
Content and E-E-A-T. Whether there is anything worth quoting, whether a named human stands behind it, and whether the claims can be checked by someone who is not you.
Schema. The full JSON-LD stack, entity identifiers, and whether the machine-readable version of your company agrees with the human-readable one.
Entity. Your name across the open web: directories, profiles, knowledge bases, and the collisions where something else owns your name.
SXO. The page types that win your money queries today, and the gap between those and what you have.
Market data. Demand by market, the keywords that are genuinely reachable, and the traffic value sitting with each competitor.
Performance. Core Web Vitals on mobile and desktop, with the render chain that explains them.
AI visibility panel. The prompt panel, run live, logged verbatim, per engine, per market.

The panel is the part nobody else hands over. Each prompt is a question your buyer actually types, and each answer is stored the way the model produced it, with the sources it leaned on. That panel becomes your baseline. If we work together, every review re-runs the same panel and reports the delta — series, not single runs, because AI answers are volatile enough that one lucky screenshot proves nothing.

what the audits keep finding

The same failures repeat across companies of every size and budget. From the published dataset:

No entity anchor. Nothing that lets a model confirm you are a real, specific company. Found in close to every audit we ran.
No machine-readable trust. Licences, jurisdictions and credentials that exist only as images or PDFs, while the negative sources about you are perfectly readable text.
Anonymous content. No named author, no dates, no sources — the exact profile a model discounts when it has to choose whom to quote.
Blocked crawlers. Often unintentional, sometimes a managed rule nobody knows about. Blocking GPTBot does not remove you from AI answers; it removes your version of the story and leaves review aggregators and competitors to tell it.
Nothing shaped like an answer. No comparisons, no pricing, no lists — the page types models quote.

The market average across those 100 audits was 35 out of 100, and 58% of sites scored 35 or below. Not one cleared 60. That is the bad news and the opportunity in the same sentence: almost any category is still winnable from a standing start.

what happens after

You get the report and the 90-day plan, and a call to walk through it. From there one of three things happens, and all three are fine by us: you hand it to your own team, you hand it to another vendor, or we run the plan together as a full-cycle retainer with bi-weekly sprints and a re-measure at every review.

The retainer starts from $2,000/mo (August 2026). The audit stays complimentary either way. We are referral-first and take a limited number of engagements, so the honest constraint is capacity, not price.

the dataset behind it

009 Agency, August 2026
what	how much
manual audits run	160+
companies mapped	2,800+
AI answers analyzed	67,200+
engines in every audit	6 — ChatGPT, Claude, Gemini, Perplexity, Grok, DeepSeek
audits published in full	100 — the report

We work across fifteen niches — Web3, FinTech, AI companies, SaaS, cybersecurity, cloud & DevOps, IT outsourcing & software development, LegalTech and law firms, iGaming, e-commerce, real estate technology, ArchViz, GameArt, consulting, and B2B service companies — so in most categories we already know what the AI shelf looks like before your audit starts.

One caveat we state everywhere: the public report is not the plan. It names the patterns we found across a hundred companies — roughly 20% of the work. The other 80% is your own numbers, and it only exists once someone measures your brand. That is what this audit is.

GEO audit