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Tutorials

26 posts in Tutorials page 1 of 3

Step-by-step playbooks, checklists, and how-to guides you can run this week.

Tutorials Product Updates

Your AI Visibility Data, Now an API: BrandGEO API v1 and the Official Laravel Client

Visibility data is only useful where decisions happen. For some teams that's the BrandGEO dashboard. For others it's a BI tool, a client report generated on a schedule, or an internal admin panel that already tracks every other marketing number. API v1 opens the platform for that second group: every brand, audit, monitor, snapshot, and trend in your account, available over REST with a bearer key. This post covers the business case, the full endpoint tour, and the official Laravel client that wraps it all in typed PHP.

Tutorials Product Updates

Put BrandGEO Inside Your Laravel Nova Admin

Most teams that run Laravel Nova live in it. It's where they check orders, users, content, and every number that matters day to day. AI visibility data shouldn't require leaving that room. Our new package, a2zwebltd/brandgeo-laravel-nova, adds a BrandGEO section to your Nova menu: monitoring KPIs, visibility trends, share of voice, competitor tables, citation sources, and full audit drill-downs, all fetched live from the BrandGEO API with your key. Here's what it looks like and how to install it in about five minutes.

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 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.