New The New AI Search Visibility & Ranking Factors Report arrives July 20th. Get notified →
AI Search Visibility: How & Why LLMs Rank You

Clarity & Direction for
AI Search Visibility
& SEO Campaigns

Large-scale correlation research built from over 350,000 LLM prompts and 150,000 search engine queries, across 100 industries and 1,100 buyer personas. Every result is cross-referenced against nearly 15 billion web pages, the entire history of Reddit, all of Wikipedia, and billions of links.

Just the signals that move the needle in your market.

100Industries Covered
350K+LLM Prompts
100B+Links Analyzed
signal_correlation.v1.report
Signalρ vs. LLM Score
Search Engine Appearances
+0.241
Best Search Engine Rank
+0.238
SE Outbound Links
+0.230
Backlink Count
+0.204
Wikidata Entities
+0.120
100
Industries
2.5PB+
HTML Analyzed
1,100
Buyer Personas
350K+
LLM Prompts
150K+
Search Queries
~15B
Web Pages Analyzed
100B+
Links Analyzed
25B+
Reddit Posts & Comments
Models Sampled
Claude ChatGPT Gemini DeepSeek GLM
Who This Is For

Data for teams too busy to build it in-house.

You know the market intelligence exists. You know it can be extracted and correlated. You just don't have the time, the team, or the crawl infrastructure to do it yourself.
01

In-House Marketing Teams

SEO, analytics, and competitive intelligence teams who need someone fluent in both marketing and engineering, able to take a data project from concept to deliverable without hand-holding.

  • SEO teams needing competitive or authority data
  • Brand teams monitoring AI and search presence
  • Analytics teams that need clean, structured datasets
  • Data teams without bandwidth for one-off projects
02

Agencies

Your client needs data and research you can't build in-house. I build it, you deliver it: clean handoff, no drama, and you set your own margin.

  • Client requests that don't justify a full-time hire
  • Complex data work outside your team's skill set
  • White-label delivery: your client, your relationship
  • Repeatable partnerships on ongoing client work
Research Process

Six independent data sources, cross-referenced.

Every industry report is built from the same pipeline: LLM sampling, then five corroborating signal sources measured against every recommended domain.
LLM

Recommendation Sampling

1,100 buyer personas across 100 industries, run against current models with over 350,000 prompts. Top recommended domains, search phrases, and on-page phrases captured per persona.

CC

Common Crawl: Web Content

Nearly 15 billion pages analyzed across Common Crawl releases. Measures general web presence and crawlability of every recommended domain.

Re

Reddit: Community Signals

The entire history of Reddit submissions and comments, scanned for domain mentions.

SE

Google Search: SERP Signals

More than 150,000 search queries, 100 results captured per query. Appearances and rank position recorded for every domain.

W

Wikimedia: Reference Signals

Wikidata entity associations plus citations and outbound links from all of Wikipedia, cross-referenced against 300 million+ entities.

BL

Web Graph: Backlink Authority

Host-level web graph with hundreds of billions of links analyzed. PageRank and Harmonic Centrality computed for every domain.

HP

On-Page Phrase Analysis

Homepage HTML downloaded for every recommended domain, parsed for persona-specific and industry phrases as a content relevance signal.

ρ

Spearman Correlation & Tiering

Each signal correlated against LLM recommendation score, per industry and pooled. Tiered Dominant → Baseline by strength.

Results from V1 of the AI Search Visibility and Ranking Factors Report (V2 coming July 20th)
All-Industry Correlations

Which signals actually predict LLM recommendations.

Pooled Spearman correlation between each of 13 technical signals and LLM recommendation score, across every industry in the dataset.
Signal Group ρ (Spearman) n Tier
Search Engine Appearances Search +0.241 5.8% 10,914 Strong
Best Search Engine Rank Search +0.238 5.7% 10,914 Strong
SE Outbound Links Search +0.230 5.3% 13,828 Strong
Backlink Count Backlinks +0.204 4.2% 20,402 Strong
BL Authority Backlinks +0.200 4.0% 19,988 Strong
PageRank Backlinks +0.194 3.7% 20,402 Confirmed
Common Crawl Coverage Web +0.123 1.5% 14,057 Confirmed
Wikidata Entities Reference +0.120 1.4% 1,619 Confirmed
Reddit Comments Social +0.111 1.2% 6,171 Confirmed
Homepage Keywords Content +0.072 0.5% 18,678 Emerging
Results from V1 of the AI Search Visibility and Ranking Factors Report (V2 coming July 20th)
145 Industries

Every industry has a different dominant signal.

Averages hide the story. Your industry's dominant signal is what your report is built around, and it isn't always what you'd guess.

Accounting Software

192 domains
Dominant
Wikidataρ = 0.515
sage.com · oracle.com · xero.com

CRM Software

115 domains
Dominant
Wikipedia Citationsρ = 0.577
hubspot.com · salesforce.com · freshworks.com

GPU / AI Infrastructure

144 domains
Dominant
Search Engine Appearancesρ = 0.535
nvidia.com · hpe.com · lenovo.com

Data Warehouse Platforms

46 domains
Dominant
Wikidataρ = 0.601
snowflake.com · databricks.com · microsoft.com

Enterprise Search & Copilots

194 domains
Confirmed
Search Engine Appearancesρ = 0.172
coveo.com · glean.com · elastic.co

Car-Wash Chains

200 domains
Dominant
Wikipedia Citationsρ = -0.700
mistercarwash.com · crewcarwash.com · take5carwashes.com
Custom Data Projects

What I build for teams.

Every project is different; here's the kind of work that comes through the door most often, on top of the standard industry reports.
CC

Common Crawl Extraction

Parse and analyze pages across Common Crawl releases at scale: structured data from HTML, URLs, tags, and page elements across billions of records.

SE

SERP Analysis & Opportunity Mining

Hundreds of thousands of search queries, every ranked URL analyzed. Contact info, partnership opportunities, and competitive gaps, extracted and structured.

CI

Competitive Intelligence

Track competitors across web properties, search results, and market signals. Build a database your team can search and act on immediately.

BR

Brand & Reputation Monitoring

Monitor search results, news, RSS feeds, and web mentions for brand terms, product names, executives, and competitors, near real-time.

LP

Lead & Partnership Databases

Searchable databases of sponsorship opportunities, link prospects, and outreach targets, nationwide or by specific location.

WS

Large-Scale Web Scraping

Phone numbers, emails, social accounts, and named entities: extracted, validated, and delivered in your preferred format.

Work With Ben Wills

26+ years across marketing and engineering.

Most marketers can't engineer complex data systems. Most engineers don't understand marketing well enough to build the right thing.

On the marketing side, I've directed teams of 70–80 people responsible for over 1,400 SEO client accounts, led international SEO campaigns across 30–40 countries, and served as the weekly point of contact for Fortune 500 accounts.

On the engineering side, I've built large-scale web scraping and indexing systems processing billions of records, written a marketing SaaS platform from scratch in pure C, and compiled the complete Common Crawl web graph history into queryable SQLite databases.

When you describe a data problem to me, I don't just understand the technical requirements; I understand the marketing objective behind it. That combination is what 26 years across both disciplines gives you.

Engineering Experience
  • Large-scale web scraping & indexing systems
  • HTML parsing and analysis (10B+ records)
  • Common Crawl parsing & extraction at scale
  • Custom database design (SQLite, key-value, sharded)
  • Built a scraper in pure C, 400M+ URLs/day
  • Firmware development (ESP32, PIC32, custom protocols)
Marketing Experience
  • Directed 70–80 person team across 1,400+ SEO accounts
  • Led international SEO campaigns (30–40 countries)
  • Weekly point person for Fortune 500 accounts
  • VP of Operations: $0 to $140K+/month in 9 months
  • SEO & PPC for SMBs, agencies, and enterprise
How It Works

Fixed scope, fixed price, built-in revisions.

Every project starts with a conversation about the business objective, not a requirements document.
01

Scope the Objective

We talk through what you're trying to accomplish, not a feature list. I define exactly what you'll receive.

02

Fixed-Price Quote

No hourly billing surprises, no scope creep. You know what you're getting and what it costs before anything starts.

03

Build & Deliver

Delivered in your preferred format (CSV, JSON, Excel, SQLite): clean, documented, ready to plug into your workflows.

04

Revisions Included

Once you see the data, you'll want it differently. That's expected; iteration is priced in from the start.

FAQ

Common questions.

What format do you deliver data in?

Whatever works for your team: CSV, JSON, Excel, or SQLite, with field-level documentation and schema definitions. Web graph databases are delivered as SQLite.

How does pricing work for custom projects?

Almost always fixed price. I scope the project, define clear deliverables, and quote a number before work begins. In-scope revisions are included.

How long does a typical project take?

It depends entirely on the project. Some are a few weeks, some are longer; you'll get a realistic timeline during the scoping conversation.

What if I need changes after delivery?

That's expected. Once people see their data, they almost always want adjustments. Iterations are baked into every project from the start.

Discuss your project.

Let me know what you're working on and we'll set up a time to talk: an industry report, a custom dataset, or both.

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