The Pedowitz Group · Revenue Marketing.AI

We measure how AI systems
decide whether to recommend you.

The AXO Diagnostic maps your site against the real questions enterprise buyers ask Claude, ChatGPT, Perplexity, and Gemini. Executive-grade analysis across the full buyer journey.

Real site crawling · Full buyer journey · 8 modules · Four LLM engines
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The Pedowitz Group · AXO Diagnostic
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AXO Intelligence Report

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Analyzing AI presence across buyer journey stages…
— The bottom line
—/100
AXO Score
—
Decide stage
Strong presence when buyers have already chosen you.
—%
Compare stage
The expensive gap. Where vendor shortlists form.
—%
Weakest persona
AI cites Forrester and McKinsey. You're absent.
Three actions that move the score
P0 · This quarter
Publish 4 named-comparison pages
6 weeks · +14 queries recovered
P0 · This quarter
Launch CFO-facing content pillar
8 weeks · +22 queries recovered
P1 · Next half
Build AI demand-gen topic hub
10 weeks · +18 queries recovered
Continue to the full analysis
Chapter 01 · Problem vocabulary

Your site speaks your language. Not your buyer's.

AI systems match buyer questions to content using vocabulary overlap. Of the six canonical terms enterprise B2B buyers use to describe your category, you cover three well. The rest live in your competitors' content.

62%
Buyer vocabulary coverage
Analyzing buyer vocabulary coverage…

Analyzing highest-scoring content…

Analyzing vocabulary gaps…

Identifying biggest gap…

Coverage by canonical buyer term
n = 6 terms · full corpus

Vocabulary gaps

Analyzing vocabulary gaps…

Generating recommendations…
→ See Chapter 06 for the content roadmap
Chapter 02 · AI presence

AI knows you at the start and the end of the buyer journey. Not the middle.

We tested 100 real buyer questions, weighted by where your journey is weakest. The presence curve has a distinct shape: strong at Aware, strong at Decide, a cavern at Compare.

Queries allocated by stage weakness 100 total · weighted inverse to your score · see all
28
18
25
17
12
Unaware Aware Compare Consider Decide
Unaware
—
Aware
—
Compare
—
Consider
—
Decide
—

Analyzing Aware stage performance…

At the Decide stage, AI cites your content when buyers have already short-listed you and are validating. That's defensive value — it matters — but it's not where the sale is made.

Analyzing Compare stage gap…

"The Compare stage is where the sale is made. It's also where ${G.site} is least present."
Synthesis · Chapters 02 + 05

Generating fix…

The highest-leverage move is comparison content. Named competitors, same template, same URL pattern. See the content roadmap in Chapter 06.
Chapter 03 · Persona variance

One persona sees you. The others don't.

Analyzing persona variance…

Analyzing weakest persona…

One persona is carrying a 31-point drag on the whole AXO score. Closing it is the single highest-leverage move in this report.
Persona variance · Module 03

Analyzing persona variance…

Generating persona recommendation…
Chapter 04 · LLM engine coverage

Four engines. Four different pictures of you.

We ran the same 100 buyer queries through Claude, ChatGPT, Perplexity, and Gemini. Each engine has different training data, different freshness signals, different retrieval biases. Your AXO score varies by — points across them. The spread reveals where to invest next.

How each cluster scores, engine by engine

Green cells are where that engine cites you strongly. Red cells are the blind spots. Engines that disagree with the pack are flagged — those cells are where the most recent content signals haven't propagated yet.

The engine where you're weakest is —, — points behind your strongest. That's the engine your next publishing sprint should be written for: fresh-indexed, canonical, well-structured. The other three will catch up for free.
→ Chapter 06 translates this into specific content investments
Chapter 05 · Competitive map

Your competitive position in AI search.

Analyzing competitive position…

AXO score vs. competitive set
n = 5 competitors · same query set

Analyzing competitor AI advantage…

Forrester's +8 delta on authority queries is not surprising. It's also not the battle to pick. Your asymmetric advantage is prescriptive implementation content, which Forrester doesn't publish and the platforms can't credibly publish.

Strategic insight loading…
Strategic framing · Module 04

White-space opportunities

Query categories being asked where no competitor owns the answer. These are the highest-return plays on your roadmap:

Loading opportunities…

These white-space plays are unclaimed. Execute in order for the highest AEO lift.
Chapter 06 · Content opportunities

Four pieces. In order. Start this quarter.

Every recommendation below is earned by a specific query gap from Chapters 01–05. No "publish more content." No content-for-content's-sake. This is the list.

01
Comparison page
Content opportunity loading…
⏱ Medium effort⌁ High impact+14 queries6 weeks
02
Authority piece
Authority content loading…
⏱ High effort⌁ High impact+22 queries8 weeks
03
Case study
Content opportunity loading…
⏱ Low effort⌁ Medium impact+8 queries3 weeks
04
Topic hub
Content opportunity loading…
⏱ Medium effort⌁ High impact+18 queries10 weeks

Each of the four above is earned by a query gap surfaced in the diagnostic. This is not a wishlist. Execute in order for maximum AEO lift this quarter.

Start with the comparison page. Lowest effort, fastest indexing in AI, unlocks the compare-stage cavern from Chapter 02. Ship by end of quarter.
Chapter 07 · Topic clusters

Your pillars are strong. Your flanks are weak.

Topical pillars emerged from the crawl. The strongest clusters are carrying your AEO score. The weaker ones need reinforcement — and they sit directly on high-intent query paths. Your 100-page AXO roadmap is allocated across clusters by inverse strength: weaker clusters get a larger share. Expand any cluster to see its allocation and the top recommended pages.

100-page roadmap · allocated by cluster weakness Inverse-strength weighted · expand all
Cluster strength · crawl-derived
Click a cluster to expand full detail

Analyzing content pillar health…

The weaker clusters are exactly where mid-funnel buyers search. That's the opportunity cost of under-investing in them.

The strongest clusters carry your score. The weaker ones are where competitors walk in.
Cluster health · Module 06
Generating recommendations…
Chapter 08 · Competitor deep analysis

A full crawl of every rival, scored on the same rubric.

Each of the five competitors above was crawled at full depth — between 540 and 8,400 pages — and scored on the same 100 queries. This is the unvarnished comparison.

Apples-to-apples · same rubric, same engines
full crawl · all engines · same rubric

Analyzing deep competitive data…

Generating competitive target…
Chapter 09 · Conversion & revenue

AI traffic converts 4.4x better than organic. The drop is at the form fill.

AI-referred visitors arrive with context the page doesn't expect. They've already had a conversation with Claude or ChatGPT. They're ready to engage. Your landing pages treat them like cold search traffic.

4.4x
AI-referred intent multiplier
Visitors arriving from LLM surfaces show 4.4x engagement vs. organic searchGA4 · referrer analysis. They also drop off 4x harder at form-fill. Mis-matched page experience.
AI-referred funnel
Composite · last 90 days
They arrived pre-qualified. Your page treated them like they had never heard of you.
Synthesis · form-fill drop

The unlock is targeted demand capture — AI-aware landing pages that acknowledge the conversation the visitor just had, not more top-of-funnel contentInternal · AI-referrer cohort.

If AXO improves and conversion drag resolves, projected pipeline recovery on current traffic mix increases significantly.
Chapter 10 · What to do next

Ship four things this half. Revisit the AXO in 90 days.

Each recommendation below is scoped to a named gap, mapped to an owner archetype, and sized in weeks. If you execute the P0s, your AXO projects to 85 within two quarters.

Every finding above traces back to one of 100 buyer questions, distributed by journey stage and tested against four LLM engines. The distribution is inverse-weighted — weaker stages get more queries so we can diagnose them thoroughly. Stages where you're already winning get lighter sampling.

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