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#Playbook

33 posts in #Playbook page 1 of 3

SEO Tutorials

How to Win the "Reddit" Searches AI Runs — Without Ever Posting on Reddit

There are two ways to influence the reddit-flavored searches that AI models run before they recommend a brand. The first is to earn genuine presence on Reddit itself — slow, community-driven, measured in quarters. The second is far less discussed: build your own pages that rank for the "[query] reddit" searches, so your content lands in the model's source set alongside the threads. This post is about the second lever — how to do it well, where the ethical line sits, and how AI brand monitoring tells you which queries to target and whether you're winning them.

SEO Tutorials

Auditing Your Own Site for AI: robots.txt, llms.txt, JSON-LD, and the Four Gates of Citation

Most AI-visibility advice points outward — earn citations, get on Wikipedia, court the review platforms. All worthwhile. But there's a cheaper, faster lever sitting right under you: your own website. If a model can't retrieve your pages, can't rank them, can't extract clean claims from them, or can't attribute those claims back to you, no amount of off-site work fully compensates. This is a practitioner's walkthrough of the on-site AI audit — the files and signals that matter, organized around the four gates an answer has to pass through to cite you.

AI Visibility Tutorials

Tracking the Queries That Matter: Keyword-Level Monitoring in the AI Era

A brand-level visibility score answers 'do AI models know us?' But buyers don't ask models about your brand — they ask about their problem. 'Best CRM for solo realtors.' 'Affordable accounting software Singapore.' 'Alternatives to [incumbent].' Whether you appear in those answers is a sharper, more commercial question than your headline score, and it deserves its own tracking. This post is about query-level monitoring: which queries to track, how to read the results per engine, and how to turn the data into work.

SEO Strategy & ROI

Where AI Gets Its Answers: Building a Citation Source Map and a Digital-PR Target List

Earning citations is the right goal, but most digital-PR programs aim blind — pitching whoever the team already knows, hoping it helps. There's a more precise way to work. When a model answers questions about your category, it draws on a finite, repeatable set of sources. If you can see which domains those are, classify them by whether they currently help you or your rivals, and find the ones that cite competitors but never you, your target list stops being a guess and becomes a map. This post is about building that map and reading it.

SEO Tutorials

The Wikipedia Lever: How a Well-Structured Entry Moves Your Knowledge Depth Score

Of every lever in Generative Engine Optimization, a well-formed Wikipedia entry has the most predictable payoff on how LLMs describe your brand. Wikipedia corpora are oversampled in nearly every major model's training data, cited heavily by search-augmented providers, and treated as a canonical fact source. Yet most brands either have no entry at all, a three-sentence stub, or an entry that was edited once in 2021 and left to rot. This is the playbook to fix that without getting your article deleted or your account blocked.

AI Visibility Industry Insights

GEO for B2B SaaS: The 5 Most Common Visibility Gaps in Early-Stage Startups

Early-stage B2B SaaS brands share a visibility profile that is so consistent it is almost diagnostic. A company under three years old, post-pivot, Series Seed to early Series A, with a small marketing function and no in-house SEO team, tends to fail the same five checks on an AI brand visibility audit. Not because founders are careless, but because the signals AI models rely on take years of patient accumulation — and early-stage companies do not have years. This piece walks through the five recurring gaps, why they happen, and what a useful first move looks like for each.