100% — 6 of 6 sampled sites met this signal.
AI SEARCH READINESS / LONDON
AI search readiness in London
A practical framework for helping London businesses become easier to find, understand, and choose across search engines and AI answer systems.
01 / THE REAL CONSTRAINT
The work starts with the system, not a checklist
The published London sample covered 6 local business websites. The market includes finance, legal services, technology, property, healthcare, and international business. The useful lesson is not a league table: each site needs clear crawler access, unambiguous business information, extractable answers, trustworthy local evidence, and a next step customers can complete.
02 / CITY SNAPSHOT
London scored differently across structure, answers, and trust
The city median score was 70.5, above the pooled 148-site median of 68. Among the 20 cities represented in the pooled file, London ranked 9 by median score. The sample is descriptive, not a census, and the score is a diagnostic summary rather than a forecast of rankings or citations.
03 / OBSERVED SIGNALS
What appeared across the 6 sampled London websites
100% — 6 of 6 sampled sites met this signal.
100% — 6 of 6 sampled sites met this signal.
83% — 5 of 6 sampled sites met this signal.
50% — 3 of 6 sampled sites met this signal.
17% — 1 of 6 sampled sites met this signal.
50% — 3 of 6 sampled sites met this signal.
04 / INTERPRETATION
Read the strongest and weakest signals together
Most common signal
100% met the “Exactly one H1” check, the most common signal in the London sample.
Least common signal
17% met the “Visible author and date” check, making it the least common of the reported readiness signals in this sample.
Answer and provenance
Direct opening answers appeared on 3 of 6 sites, visible author-and-date signals on 1, and citations on 3. These checks address different trust and extractability questions: a concise answer helps retrieval, named responsibility supports provenance, and citations make important claims easier to inspect.
Crawler handling
None of the 6 sampled sites blocked a named AI crawler in the captured robots.txt files. 0 explicitly named one or more AI agents, which indicates deliberate configuration but does not by itself establish content quality or visibility.
05 / RESEARCH BOUNDARY
What this London sample can—and cannot—show
The scan records visible technical and editorial signals from 6 business websites at one point in time. It can reveal recurring implementation gaps and useful questions for an audit. It cannot prove market-wide prevalence, customer preference, future AI citations, or the commercial impact of any single signal.
The pooled dataset contains 148 businesses across 20 cities. Raw rows, field definitions, and the methodology are published so readers can inspect the basis of the comparison rather than relying on an unsupported city ranking.
06 / PUBLISHED ROWS
The London evidence behind the aggregate
The table keeps the city page auditable at row level. Scores summarize the published checks; the three provenance columns show whether a direct answer, visible author-and-date signal, and citations were observed during the scan. They are diagnostic observations, not endorsements of the listed businesses.
Row-level field notes
On thegoodmarketer.co.uk, the scan counted 5 JSON-LD block(s) and found scannable list/table support. Named authorship and dating did not appear; the page-level score was 91.
At pearllemon.com, section hierarchy met the check; paragraph length met it as well. The page showed author-and-date signals and received 73 overall.
makeagency.co.uk scored 73; its opening contained 32 words, and the scan found a direct answer. Visible author-and-date responsibility was not observed.
influencedigital.co.uk shows why a single score needs context: 2 JSON-LD block(s), no question-led headings, scannable list/table use, and missing author/date signals produced 55.
londonmarketingcompany.co.uk scored 68; its opening contained 67 words, and the scan found a direct answer. Visible author-and-date responsibility was not observed.
For passion.digital, the captured page exposed 2 JSON-LD block(s). Schema passed the published check, while citations were not observed; the composite score was 64.
| Sampled domain | Score / 100 | Direct answer | Author + date | Citations |
|---|---|---|---|---|
| thegoodmarketer.co.uk | 91 | Observed | Not observed | Observed |
| pearllemon.com | 73 | Not observed | Observed | Observed |
| makeagency.co.uk | 73 | Observed | Not observed | Not observed |
| influencedigital.co.uk | 55 | Not observed | Not observed | Not observed |
| londonmarketingcompany.co.uk | 68 | Observed | Not observed | Observed |
| passion.digital | 64 | Not observed | Not observed | Not observed |
07 / AUDIT QUESTIONS
Start with the least common London signals
Only 17% of this London sample met the check. On the website being audited, identify which priority templates fail it, whether the field exists in the CMS, who owns the correction, and how the rendered release will be validated.
Only 50% of this London sample met the check. On the website being audited, identify which priority templates fail it, whether the field exists in the CMS, who owns the correction, and how the rendered release will be validated.
Only 50% of this London sample met the check. On the website being audited, identify which priority templates fail it, whether the field exists in the CMS, who owns the correction, and how the rendered release will be validated.
Do not optimize one check in isolation. Test whether accessible pages, clear answers, named responsibility, supporting sources, local business facts, and a useful conversion path describe the same organization consistently.
08 / NEXT DECISION
Move from city evidence to the specific website
Which pages should be tested first?
Begin with the service, product, category, location, expert, and conversion pages that influence the most valuable customer decisions. The city sample cannot choose those priorities for you.
How should a failed check become implementation work?
Record the affected template, rendered evidence, intended customer outcome, technical or editorial owner, dependency, acceptance criteria, release date, and post-release observation.
When should the city comparison be ignored?
Ignore it whenever the site’s direct evidence, market, regulation, platform, or customer journey makes a different problem more material. The research is a question generator, not a universal roadmap.
INTERPRET THE CITY SAMPLEPARENT STUDYCompare the wider city-market sample METHODUnderstand collection and limitations LOCAL SEARCHApply findings to a local-search system COMPARE ANOTHER MARKETDubai: 8 Local Businesses Scanned COMPARE ANOTHER MARKETSingapore: 7 Local Businesses Scanned RESEARCH DIRECTORYContinue into published studies
Put the London findings in research context
This page reports observations from 6 sampled businesses in London. It is a research snapshot, not a claim that MoxSEO has an office in the city and not a score for every local business. Compare the result with the parent study, inspect the method, and test the same signals on the specific organization you are evaluating.
Review the study design and cross-market findings before interpreting one city’s sample in isolation.
Inspect how observations were collected, what the sample can support, and what it cannot prove.
Connect entity clarity, business information, location pages, reviews, and conversion paths.
Use another city sample as a comparison point, not as a universal benchmark.
Use another city sample as a comparison point, not as a universal benchmark.
Explore crawler access, answer extractability, and related search-system research.
09 / NEXT STEP
Improve search and AI readiness in London
Bring the site, the goals, and the constraints. We will help define the most useful first move.