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Engagement 01 — Entry product

How visible are you in AI search, and what is stopping you?

A fixed-scope diagnostic that ends with a number you did not have, a competitor set you probably did not expect, and a prioritised list of the specific things causing the gap.

Request an auditSee an example audit

The question it answers

One question, answered with evidence.

How visible is this company in Google and AI search, and what is preventing it from being discovered, cited or recommended? Everything in the audit exists to answer that, and anything that does not is left out.

Format
Fixed scope, fixed fee, delivered once.
Typical duration
Two to three weeks from access being granted.
Deliverables
Findings with replayable evidence, prioritised recommendations with owners and effort, a query-level visibility baseline you keep, and a walkthrough call.
What you provide
Search Console and analytics read access, one 45-minute session with someone who knows the product, and your own competitor list.
What happens after
Either you run the plan yourself — genuinely fine, the report is written to be executable by your team — or we run it as AI Search Growth.
Why it is paid
Because doing it properly costs real hours and real model spend, and because free audits get the attention free audits deserve.

Pipeline

How the audit runs.

  1. 01

    Crawl

    automated
    Full site crawl respecting robots.txt, with raw HTML retained so every finding can be re-derived without re-crawling.
  2. 02

    Analyse

    automated + human
    Technical SEO, content, structured data and entity analysis run in parallel over the crawl output.
  3. 03

    Build the competitor set

    human confirmed
    Client-declared, market, SERP and AI-answer competitors. The last list is the one that reframes the conversation.
  4. 04

    Generate the query set

    human confirmed
    Queries derived from your industry, product, customers, geography, use cases and competitors — then reviewed with you before anything is executed.
  5. 05

    Execute

    automated
    Every query, every surface, repeated. Raw responses stored verbatim with model, timestamp, geography and settings.
  6. 06

    Extract

    automated + sampled review
    Mentions, positions, citations, competitors named, source types. Low-confidence matches are queued for a human to check.
  7. 07

    Trace the evidence chain

    human led
    For queries where a competitor wins and you do not appear, read the cited sources and work out what they say that you have no equivalent of.
  8. 08

    Recommend and review

    human owned
    Findings become prioritised recommendations. Nothing reaches you without a named person reviewing it.

Scope

Eight modules.

Each one produces findings with evidence attached. Findings that do not connect to a page someone buys from, or a query someone searches, do not make the report.

Technical SEO

  • Crawlability and indexability
  • robots.txt, per-bot directives, meta robots
  • HTTP status, redirects, canonicalisation
  • Sitemap correctness
  • Internal linking and click depth
  • Metadata and heading semantics
  • JavaScript rendering, fetched versus rendered
  • Mobile usability
  • Page speed and Core Web Vitals where field data exists

Website content

  • Topic and intent coverage
  • Customer questions actually answered
  • Commercial, comparison and informational query coverage
  • Content gaps against the tracked query set
  • Topical authority assessment
  • Thin, duplicate and outdated content

Structured data

  • Organization, Product, Service, SoftwareApplication
  • Article, FAQ, Breadcrumb, Person, Review
  • LocalBusiness where genuinely relevant
  • Validation errors and markup that contradicts the page
  • sameAs coverage
  • Explicitly: no schema recommended for its own sake

Entity and brand

  • Company name consistency across every property
  • Description and category consistency
  • Products, services, founders, locations
  • Social and platform profiles
  • External references and brand mentions
  • Disambiguation from similarly named companies

Authority

  • Backlinks and referring domains
  • Industry publication presence
  • Review platforms including G2 and Capterra
  • Directories that are actually maintained
  • Reddit and practitioner communities
  • Podcasts, newsletters, interviews, news
  • Partner sites and expert mentions

Competitors

  • Competitor set built four ways, including from AI answers
  • Rankings and content footprint
  • Authority and referring domains
  • Mentions and citations
  • AI visibility head to head
  • Third-party presence comparison

AI search

  • 42+ queries generated from your industry, product, customers, geography and competitors
  • Executed across ChatGPT, Gemini and Perplexity
  • Google AI experiences where technically observable
  • Each query repeated 3 times because answers are non-deterministic
  • Raw responses stored verbatim and replayable
  • Mention, position, citation and competitor extraction

AI accessibility

  • OAI-SearchBot, GPTBot, ChatGPT-User, PerplexityBot, Google-Extended
  • What each token actually controls
  • Edge and WAF behaviour against declared crawler user agents
  • Server-rendered versus client-only content on commercial templates
  • Treated as a separate diagnostic from visibility, because it is a separate problem

Output

What the deliverable looks like.

Observation, interpretation and recommendation are kept apart. Every rate shows its denominator. Every figure carries a label saying whether it was observed, verified, inferred or estimated.

Sample dataFictional company, fictional competitors, hand-authored numbers. Shown to illustrate the output format of a real audit, not the results of one.

Visibility baseline, by surface

ChatGPTDemo

16.7%

7 of 42 queries

Citation rate
4.8% (2/42)
Avg. position
5.4
Executions
126
GeminiDemo

26.2%

11 of 42 queries

Citation rate
11.9% (5/42)
Avg. position
4.1
Executions
126
PerplexityDemo

21.4%

9 of 42 queries

Citation rate
14.3% (6/42)
Avg. position
4.8
Executions
126

Against the competitor set

Demo
  • Spendwise54.8% (23/42)
  • Tallyflow45.2% (19/42)
  • Obol Pay28.6% (12/42)
  • Ledgerloopyou21.4% (9/42)
  • Kestrel Finance19.0% (8/42)

Findings — observation only, interpretation kept separate

  • blockerai-accessibility

    Comparison pages return an empty container to non-browser clients

    Fetching /compare/* without JavaScript execution returns a document whose main content area contains no product data. The same URLs render fully in a browser.

    • → curl of 6 /compare/* URLs: 0 product rows present in returned HTML
    • → Rendered DOM in headless browser: 14 product rows present
  • blockerai-accessibility

    Edge bot rule challenges non-browser user agents on two commercial templates

    Requests to /pricing and /compare/* with non-browser user agents receive a 403 with a JavaScript challenge body.

    • → HTTP 403 on 8 of 8 requests with a declared crawler user agent
    • → HTTP 200 on the same URLs with a standard browser user agent
  • highcontent

    No alternatives page exists for the most-tested competitor

    The query 'spendwise alternatives' is in the tracked set at priority 5. No page on the site targets it. A competitor's own alternatives page was cited in 4 of 9 executions.

    • → Site crawl: 0 pages matching alternatives intent
    • → Query q-02: subject mentioned in 4/9 executions, own domain cited in 1/9
  • highentity

    Company description varies across five external profiles

    The one-line description differs materially between the website, two review platforms, the LinkedIn profile and a directory listing. Two describe the product as accounts-payable software rather than expense management.

    • → 5 external profiles collected
    • → 3 distinct category descriptors in use
Walk through the full example

From findings to revenue

Prioritised actions, then the numbers that pay for them.

Findings are worthless without an order of operations, and visibility is worthless if it never reaches pipeline. Both are part of the deliverable.

Sample data. A fictional company and invented probes: the format of the deliverable, not a client result. The metric code is real; the inputs are not.

Recommendation engine

impact x confidence / effortSample data
  1. 01

    Fix Organization schema and align entity data across external profiles

    Your Organization markup omits sameAs, founders and a canonical description, and three external profiles describe the company differently. Inconsistent entity data makes it harder for a model to resolve you as one company.

    seoimpact higheffort lowconfidence highEngineering
    7days 0-30
  2. 02

    Make pricing information crawlable and specific

    Pricing sits behind a form. Every pricing query in the probe set was answered using third-party estimates, which were wrong. Publishing a real pricing explainer replaces a guess with your own words.

    contentimpact higheffort lowconfidence highProduct marketing
    7days 0-30
  3. 03

    Instrument AI referral traffic and connect it to pipeline

    Visibility that cannot be traced to sessions and leads will not survive a budget review. Instrumentation must precede the growth claims.

    growthimpact higheffort lowconfidence highGrowth + engineering
    7days 0-30
  4. 04

    Publish a genuine comparison hub for the three queries you lose most

    Answer engines pull shortlists from pages that compare options explicitly. You have no page that names an alternative, so there is nothing to quote when a buyer asks for a shortlist.

    contentimpact criticaleffort mediumconfidence highContent lead + product marketing
    5.56days 0-30
  5. 05

    Add internal links from the blog into the six pages that can win answers

    Your highest-intent pages sit four clicks deep with almost no internal links. Depth suppresses both crawling and perceived importance.

    seoimpact mediumeffort lowconfidence highContent lead
    4days 0-30

The priority score is published, not proprietary: impact x confidence, divided by effort. A client who disagrees with the ordering can argue with the inputs, which is the point.

Visibility to revenue

Month over monthSample data
  1. AI answers naming you14of 40 queries
  2. AI answers citing your site9of 40 queries
  3. AI referral sessions486sessions / month
  4. Organic sessions8,420sessions / month
  5. Qualified leads37leads / month
  6. Pipeline created148,000USD / month

Nobody buys “you appear 12% more often in AI answers”. The chain has to end in pipeline, and the parts of it that are estimates rather than measurements are labelled as estimates in the real report.

Honesty

What the audit will not tell you.

  • It will not tell you what any AI provider’s ranking logic is. Nobody outside those companies knows, and we will not pretend to.
  • It will not promise that fixing the findings produces a mention. It identifies the reasons you are currently excluded and establishes a baseline to measure against.
  • It will not report a rate computed from a sample too small to mean anything. Where the denominator is small, we show it and say so.
  • It will not claim a model said something unless the response is stored and replayable.

Next step

Start with the audit.

Two to three weeks, fixed scope, and a baseline you keep whether or not you continue with us.

See the recurring engagement