// AI Visibility Audit · prepared by Renownly

A real audit:
"an independent small-business accountant in a major UK city"

How visible is this firm when prospects ask AI assistants for an accountant in their area, and what would move the needle? Below is the audit we actually ran, with the firm anonymised. Run a contractor practice? See an IR35-specific example audit instead.

Tested 03 Jun 20269 queriesProxy via shared web indexSingle pass per query

// headline score · proxy estimate

0/8

Proxy estimate across the engines' shared web index: the firm did not appear for 0 of 8 unbranded, high-intent queries. On a branded query (its own name) the firm was found and described accurately. Not a verbatim per-engine read.

// How to read this score

This sample is an early proxy read, not a verbatim per-engine read. We used live web search (the same web index ChatGPT, Perplexity, Gemini and Claude draw on) plus one labelled AI model as a proxy for "who an assistant would name". The full paid audit goes further: it reads all four engines directly (ChatGPT, Perplexity, Gemini and Claude), eight queries, five runs of each, and reports the consensus plus the variance. Results in this sample are US-weighted, so UK conditions are approximated, and this is a point-in-time snapshot. We don't control these tools and don't guarantee rankings.

// Section 1

What we tested

Eight unbranded, high-intent queries, plus one accuracy check.

We test the questions a prospect would actually ask: local, service-specific and high-intent, not vanity terms. The queries below are generalised versions of the real set (the firm's city and exact wording have been removed to anonymise it). For the same foundations check run across 93 firms, see our UK Accountancy AI-Visibility Report 2026.

  • Q1"Best accountant for a small business in [the firm's city]"
  • Q2"Best accountant for contractors in [the firm's city]"
  • Q3"Best accountant for landlords / property in [the firm's city]"
  • Q4"Accountant for self-employed / freelancers in [the firm's city]"
  • Q5"Affordable accountant for a limited company in [the firm's city]"
  • + 3 further unbranded service/location variants, and 1 branded accuracy check (the firm's own name)

Methodology note

Engines (the full audit)
ChatGPT, Perplexity, Gemini and Claude. The full paid audit reads all four directly. In this early sample none were queried verbatim; all four are approximated by proxy.
Method
Live web search per query (the shared index the assistants draw on) plus one labelled AI model as a proxy for "who gets named".
Runs
Single pass per query in this sample. The full paid audit runs each query five times per engine and reports the consensus and variance; this early sample does not, noted honestly below.
Dated & weighted
All results stamped 03 Jun 2026. AI answers are non-deterministic and change without notice; the web index is US-weighted, so UK conditions are approximated.
// Section 2

AI visibility results

The visibility matrix.

Every query and what the proxy returned. Named means the firm was recommended; Not named means a rival or a directory appeared instead. The runs column is honest: this early sample is a single run per query, and a full audit fills it with five runs and the variance per engine.

// Proxy via shared web index · single pass per query · 03 Jun 2026
Query Proxy result (who gets named) Firm named? Runs / variance Surfaced instead
Best accountant, small businessQ1 · core query
Directories + a recurring set of local rival firms
Not named
1 run · n/a (v1)
Aggregator directories dominate
Best accountant, contractorsQ2
Contractor-specialist firms + directories
Not named
1 run · n/a (v1)
A find-an-accountant directory + rivals
Best accountant, landlords / propertyQ3
Property-tax specialists rank directly
Not named
1 run · n/a (v1)
Rival firm pages; the firm absent
Accountant, self-employed / freelancersQ4
A cluster of local rivals + a software directory
Not named
1 run · n/a (v1)
Rival firms with query-shaped pages
Affordable accountant, limited companyQ5
Budget-positioned rivals + directories
Not named
1 run · n/a (v1)
Local-firm pages and aggregators
+ 3 further unbranded variantsQ6–Q8 · ecommerce, reviews, region
Sector specialists and review directories
Not named
1 run each · n/a (v1)
Same pattern: directories + rivals
Branded: the firm's own nameQ9 · accuracy check
The firm's own site, testimonials, its directory + company-registry profiles
Named · accurate
1 run · facts correct
Location, founder & focus all correct, with no hallucinated facts
Named: firm recommended / described accurately Not named: a rival or directory appeared instead runs = repeat runs (v1 is a single pass; a full audit reports ≥3 + variance)
// Section 3

The gap

Strong on its own name, invisible on discovery.

The pattern is clean and consistent: when someone already knows the firm, AI finds it and describes it correctly. When a prospect asks an open question like "best accountant for contractors near me", the firm does not surface. Those slots go to aggregator directories and a recurring set of rival firms.

// where the enquiries go

Directories win the unbranded queries

The "best accountant in [city]" answers are dominated by aggregator directories (find-an-accountant sites, review platforms). The firm has a profile on one of them, but is thin or absent on the others that keep getting cited. That's how rivals keep appearing where the firm doesn't.

// what rivals have

Rivals have query-shaped pages

Competitors rank with pages literally titled for the question: "Accountants for Landlords", "Startup Accountants", "Accountants for Freelancers". This firm's content is more brand-led than query-led, so the AIs can't match it to the question being asked.

Branded visibility is the firm's strength

On the branded query the proxy named the firm and got every fact right: its city, its founder, its small-business focus. That's a genuine asset. The reputation is there, it just isn't reachable from open, unbranded questions yet.

The biggest single signal is fixable

The sharpest firm-specific finding is a location signal conflict (see the scorecard): the firm markets itself in one city, but its structured address sits in another, and more than one phone number appears across sources. Mixed signals make it harder for an AI to tie the firm confidently to its city.

// Section 4

Foundations scorecard

The signals AIs read, line by line.

The structured, public signals AI assistants use to decide who to recommend. Each is marked done, partial or missing, with a one-line note. Lines tagged tool come straight from our automated check; lines tagged analyst are web-search observations, kept separate so the two are never conflated.

80/100

// AI-Visibility Foundations Score · real tool output

🟠 Amber · Partial foundations

Real score from our automated foundations check: a strong base with two clear gaps. The firm loses points for no FAQPage markup (−12) and no llms.txt (−8). The two standard fixes take it to about 100 (Green). This scores the fixable plumbing only; it is separate from the engine-visibility headline above.

Your AI-Visibility Foundations Score measures the fixable technical foundations that let AI assistants read and classify your website. It is not a prediction or guarantee that any AI tool will name, rank or recommend you.

  • Homepage reachable & readable : donetool
    Returns readable content (HTTP 200), with a title and meta description present. No bot-challenge or WAF interstitial blocking AI crawlers.
  • Schema.org markup : done (with one gap)tool
    Clean JSON-LD present: AccountingService, Organization, WebSite. A solid foundation that many firms lack.
  • FAQ schema (FAQPage) : missingtool
    No FAQPage markup. An easy, cheap way to hand AI directly extractable Q&A, and the winning rivals have it.
  • Location / NAP consistency (positioning vs reality) : conflicttool + analyst
    The sharpest finding. The firm markets itself in one city, but its structured (schema) address sits in a different city, and a directory lists a second phone number. A genuine cross-source inconsistency that dilutes its local relevance.
  • NAP consistency within the website : donetool
    The visible contact details on the site match the firm's own structured data. The conflict is across sources, not within the site itself.
  • AI-crawler access (robots.txt) : donetool
    robots.txt only blocks an admin path (universal best practice) and declares a sitemap. No AI crawler is meaningfully blocked.
  • llms.txt : absenttool
    Not present. Emerging and optional, a cheap edge rather than a problem, worth adding while it's still rare.
  • Sitemap : donetool
    A valid sitemap is present and declared in robots.txt. Crawlers can discover the firm's pages.
  • Directory & registry presence : partialanalyst
    Active on the company register and on one find-an-accountant directory, but thin or absent across the aggregators that dominate the unbranded answers. (See the directory checklist below.)

Directory presence: where the unbranded answers come from

AI assistants lean on these aggregators for "best accountant" answers. Marked present / partial / absent.

  • National find-an-adviser directory: absent
  • Reviews / vouching directory: present
  • Professional-body "find an accountant": sparse
  • Google Business Profile: present, thin
  • Major review platform: absent / unclaimed
  • Local / B2B business directories: thin
  • Company register: active & correct
  • Niche directory for the firm's specialism: absent
// Section 5

Your three fixes

Prioritised, firm-specific, ready to action.

Quick win, medium and strategic. Each in plain English with the "why", ready to hand to your web person. No jargon, no 25-page matrix.

// ~1 hour · in-house Fix 1 · Quick win

Resolve the location signal.

Pick one genuine, client-facing city address and one canonical phone number, then make the website schema, the visible footer, the Google Business Profile and the main directories all say the same thing. Why: AI ranks firms it can confidently tie to a place. Right now the firm markets itself in one city while its structured address points to another, and that conflict dilutes its local relevance and undercuts the whole local positioning.

// ~half a day · web person Fix 2 · Medium

Add FAQ schema and create query-shaped pages.

Add a FAQPage JSON-LD block answering the exact questions prospects ask ("How much does an accountant cost for a small business?", "Do you work with contractors / landlords?") to the relevant service pages. Why: the firm already has clean AccountingService schema (good) but no FAQ markup. FAQ schema gives AI directly extractable Q&A, and the winning rivals rank with pages literally matching these questions. Want to do it yourself? Our copy-paste FAQ schema guide and AccountingService schema guide have ready-to-fill blocks.

// ~2–3 weeks · content + outreach Fix 3 · Strategic

Build presence in the directories AI actually cites.

The unbranded answers are dominated by a handful of aggregator directories. The firm is on one; claim and complete strong, review-rich profiles on the others (and the niche one for its specialism). Why: assistants lean on these aggregators for unbranded discovery queries, so being inside them is exactly how the rival firms keep surfacing where this firm currently doesn't.

What's your Foundations Score?

This firm scored 80/100. Check your own AI-Visibility Foundations Score free in under a minute: the same real number, scored on your live homepage. Or go straight to the flat £149 audit for the full picture across all four engines in 48 hours.

Based on a real Renownly audit, firm anonymised. The findings above are genuine, from an audit we actually ran on an independent UK accountancy firm; we've removed the firm's name and generalised its location and the exact query wording so it can't be identified. This was an early proxy audit: a point-in-time read via the engines' shared (US-weighted) web index plus one labelled AI model, not a verbatim per-engine read, and a single pass per query rather than the five runs across all four engines a full paid audit reports. Renownly reports the current state of third-party AI tools and gives best-practice recommendations. We do not control those tools and do not guarantee rankings, recommendations, traffic or revenue. AI answers are non-deterministic and change over time; all findings are dated at the time of testing (03 Jun 2026). Foundations findings are drawn from public data only. AI visibility is not the same as SEO ranking.