A buyer-question map for your category
Fifteen to twenty questions a buyer types the week they are ready to spend, asked across ChatGPT, Perplexity, and Google, in clean sessions, with every named company and cited source recorded per answer.
Find where AI search is sending your buyers to competitors A measured map of the questions, the sources, and the gap worth fixing.
Your SEO reports show rank and traffic. They do not show what ChatGPT, Perplexity, and Google AI Overviews say when a buyer in your category asks who to use. This engagement measures that answer, names the sources behind it, and tells you whether the gap is worth paying to close.
AI search optimization for B2B SaaS starts with measurement, not content. I map the high-intent questions your buyers ask ChatGPT, Perplexity, and Google, record which companies and sources appear in each answer, identify where your pages lose, and prioritise the smallest repairs worth making. No ranking or citation guarantee is offered, because none can be measured in advance.
What is this actually costing you today?
You have said some version of this
- "We rank on page one and a competitor still comes up when someone asks ChatGPT."
- "Our SEO agency reports traffic. Nobody reports what the AI answers actually say."
- "We bought a visibility dashboard and it gives us a score with no next action."
- "I do not know whether this is a real revenue problem or a fashionable one."
What it costs while it stays unfixed
- Unseen every buyer who asked an assistant for a shortlist and never reached your site, because no rank report records that answer.
- 9 of 16 buyer questions in my own category where Perplexity named no vendor at all. Empty answers are cheap to win and nobody is measuring them.
- Unfalsifiable a GEO or content spend with no baseline pull, which means nobody can show whether it moved anything.
What do you actually get at the end?
Share of answer, by engine, with the sources behind it
Who appears, how often, and which pages the engines are pulling from. Engines disagree sharply, so the number is reported per engine rather than averaged into one score.
Where your pages lose, and why
For each question you are absent from: whether the cause is crawler access, no quotable passage, no corroborating source, or a competitor page that simply answers it better. Observation, evidence, and inference are labelled separately.
A prioritised repair list, smallest first
The empty questions and the near misses come first, because they cost a good page rather than a year. You receive the list in writing whether or not you hire me to do the work.
Does any of this actually work?
These are my own numbers, on my own systems, with the measurement date and instrument named so you can re-run them. They are not client case studies, because I will not put a testimonial on this page before I have one worth quoting.
3 of 47 answers that named my own product across three engines
I ran 16 buyer questions for my own category through ChatGPT, Perplexity, and Claude and published the result, including the bad number. Semrush appeared in half the answers. That is the same measurement I run for you.
9 of 16 Perplexity answers that named no vendor at all
More than half the buyer questions in the same study had no incumbent on Perplexity, and six of sixteen had none on ChatGPT. Those are the questions to win first, and a rank report cannot see them.
61% of AI Overview citations came from pages outside the organic top 5
36 of 59 sources cited in Google AI Overviews for eight commercial AI-services queries were not in the organic top five for that query. Ranking well does not decide who gets quoted, in either direction.
How does this compare to the alternatives?
| AI Discovery Review | Rank and traffic SEO reporting | AI visibility dashboard | |
|---|---|---|---|
| What it measures | Who the engines name for your buyer questions, and from which sources | Position and sessions, or a visibility score with no source list | |
| Tells you which questions are empty | Yes, per engine | No | |
| Explains why a page loses | Yes, cause labelled per question | Score only | |
| Recommends the smallest repair first | Yes, in writing | Generic content recommendations | |
| Promises rankings or citations | No. Measured before and after, noise floor stated | Often, and rarely with a baseline | |
| Typical cost | Free 30-minute review, then $1,500 diagnostic | $2,000 to $10,000 a month, or $100 to $500 a month for a dashboard | |
What does it cost?
Free review, then $1,500 diagnostic
Start with a free 30-minute AI Discovery Review: send your domain and your three closest competitors, and I run a sample of buyer questions before the call so we look at real answers, not a pitch. If the sample shows a gap worth measuring properly, the Citation Gap Diagnostic is $1,500, fixed scope, delivered in five business days. After that, and only if the findings justify it, a $4,500 four-week repair sprint and a $1,500 a month measurement retainer are available. The diagnostic fee comes off the sprint if you book within 14 days.
Book an AI Discovery Review →Or DM me "AI VISIBILITY" on LinkedIn and send your domain
Or email hello@chudi.dev directly.
NO DISCOVERY-CALL FUNNEL · NO RETAINER LOCK-IN · WRITTEN SCOPE BEFORE ANY INVOICE
Questions people ask before hiring me
Who is AI search optimization for B2B SaaS actually for?
Companies that already invest in SEO or content, sell to buyers who research before they buy, and cannot currently see what ChatGPT, Perplexity, or Google AI Overviews say when those buyers ask for a shortlist. If you have no organic programme at all, fix that first; this work sits on top of it, not instead of it.
How is this different from the generative engine optimization page?
That page describes the implementation work: making pages quotable, fixing schema and crawler access, running the sprint. This page describes the diagnosis that decides whether any of that is worth buying. Most companies should start here, because the sample sometimes shows there is no gap worth paying to close.
What exactly gets measured?
A fixed set of fifteen to twenty buyer questions for your category, asked in clean sessions on each engine, with every company named and every source cited recorded per answer. Brand names are matched as well as domains, because engines say a company name far more often than its web address. Results are reported per engine, never averaged.
Can you guarantee we will be cited or ranked?
No, and I would not trust anyone who does. Citation presence is volatile between pulls and engines change how they select sources. What I can promise is a dated baseline, a repeat measurement with the same method, and a plain statement of whether any change is inside the noise floor.
What do I need to send before the free review?
Your domain, three competitors you lose deals to, and the two or three questions a buyer asks the week they are ready to spend. I run a sample before the call. Thirty minutes is enough to see whether the gap is real, where it sits, and whether the paid diagnostic is justified.
What happens after the diagnostic?
You receive the question map, the per-engine share of answer, the cause labelled for each question you are absent from, and a repair list ordered smallest first. You can hand that list to your own team or an existing agency. If you want me to do the repairs, the four-week sprint and the monthly measurement retainer exist, and the diagnostic fee comes off the sprint.
When is this the wrong purchase?
When your category has no buyer questions being asked of AI assistants yet, when you have no pages that could plausibly answer them, or when your sales cycle is driven by outbound rather than research. In those cases the review will say so and you should spend the money on the constraint that actually binds.
Why should you be the one to run this?
I run the same measurement on my own products and publish the numbers, including the ones that make me look absent. The studies quoted on this page are mine, dated, and reproducible. That is the only kind of evidence I will accept from a vendor, so it is the only kind I offer.