Industries

The verticals where proof comes first.

I work in four verticals where I have shipped and measured the outcome, not just pitched it. Each section below explains the pattern I see in that market, the system I deploy, and the published evidence behind it.

01

B2B SaaS & MarTech

Client work

The pattern

Buyers compare your product inside ChatGPT and Perplexity before they ever fill a demo form. If your comparison pages, alternative pages, and documentation are not extractable, the shortlist forms without you.

What I deploy

Technical crawl architecture, bottom-funnel comparison content, and entity schema that keeps the product inside the answer, backed by founder narrative that makes the category feel owned.

+200%
Organic traffic, FigPii, 12 months
211%
Organic clicks, year over year
#1
Rankings for CRO and UX terms
100+
Posts published and ranking
02

Data & Analytics Consultancies

Client work

The pattern

Deep expertise gets buried under jargon and vendor content. Consultancies lose high-intent searches to the analytics tools they implement, even when the consultancy has the deeper practitioner answer.

What I deploy

An editorial engine mapped to analytics intent, practitioner teardowns, partner co-marketing, and schema that makes the consultancy citable across Google and LLM answers.

4.3M+
Search impressions over 16 months
2x
Organic clicks, 6K to 12K in 6 months
33%
Average newsletter open rate
43
Podcast episodes produced
03

HR, Talent & EOR Tech

Client work

The pattern

A crowded category where every platform claims global coverage. Buyers ask AI for comparisons, and the founder becomes the signal AI can actually verify across independent sources.

What I deploy

Founder-led narrative, executive ghostwriting, and entity triangulation across LinkedIn and the owned domain, tied to category-defining points of view rather than generic product updates.

251K
LinkedIn impressions in 90 days
8,423%
Impression growth vs prior period
62.6K
Impressions on one AI Agents post
44.3K
Impressions on one RAG post
04

High-Ticket Local & Real Estate

Client work

The pattern

Local buyers now ask ChatGPT for recommendations instead of scrolling Maps. The proximity pack gets skipped, and the answer is decided by entity consensus, reviews, and structured local content.

What I deploy

Local GEO: entity schema, neighborhood and pillar content, review and citation consensus, plus conversion-first service pages that turn an AI recommendation into an inquiry.

#1
AI-cited domain, Stafford
32.6%
Citation usage across 100 prompts
$100K+
Transaction sourced via ChatGPT
93/100
NeuronWriter score vs 77 competitor
Fit

Where I am a fit, and where I am not

Strong fit

  • ✓B2B teams with real expertise and a sales cycle longer than one click.
  • ✓Companies whose buyers already ask AI for comparisons or recommendations.
  • ✓A team that can give access to analytics, Search Console, and the people who know the customers.
  • ✓Local or high-ticket businesses where one qualified lead pays for the engagement.

Not a fit

  • ×Catalog-scale e-commerce with hundreds of thousands of SKUs and no engineering support.
  • ×Teams looking for daily volume production without a review process behind it.
  • ×Projects with no analytics access, where success cannot be measured.
  • ×Anyone who wants keyword coverage instead of a system that produces qualified demand.

Tell me your vertical and the buyers you are missing.

Start with a direct 30-minute diagnostic, or share your domain for a free 3-point search teardown.