Guides
Getting cited in AI search
When a buyer asks ChatGPT, Perplexity, Claude, or Gemini about your category, the answer names a short list of products and cites a handful of sources. If you are not in those sources, you are not in the answer. These guides cover the two halves of that problem: measuring where AI recommends competitors instead of you, and actually closing the gap.
We compare the monitoring tools head to head, explain what each one is genuinely best at, and show where almost all of them stop, at the report. Start with the comparison closest to your situation, or run a free check on your own domain to see your losing prompts first.
Alternatives to Brandlight for AI recommendation monitoring
Brandlight is built for enterprise. Here are the real alternatives for AI recommendation monitoring, what each is for, and how to actually get cited.
Best tools to track AI search visibility for your company
The best tools to track your company's AI search visibility, what each one actually does, and the one that closes the gap instead of only reporting it.
Best AI visibility tools for SaaS companies
The AI visibility tools that fit how SaaS actually gets bought, and the one built to win the category and comparison prompts that drive pipeline.
Tools to monitor brand mentions in AI-generated content
How to monitor your brand mentions across AI-generated answers, the tools that do it, and the one that turns missing mentions into earned ones.
Gumshoe vs Otterly for brand tracking
A straight comparison of Gumshoe and Otterly for AI brand tracking, what each is best at, and the option both of them leave on the table.
Profound vs AthenaHQ: an AI visibility review
Profound versus AthenaHQ for AI visibility, where each one wins, and the capability both of these enterprise tools stop short of.
Tools to measure B2B brand visibility in generative AI
How to measure B2B brand visibility in generative AI, the metrics that matter, and the tool that turns the measurement into pipeline.
Structured data is quietly deciding who gets cited by AI
Schema and structured data are among the strongest signals for getting cited in AI answers. The evidence, and how to make your pages citable.
AI engines trust editorial content over your product pages
For the buyer prompts that drive deals, AI engines lean on third-party editorial and review sources, not your owned pages. What that means for your strategy.
The test that predicts whether AI will cite your page
A practical framework to predict whether AI engines will cite your page, built on page speed, schema, and topical authority. Score yourself in minutes.
How to build an autonomous marketing operating model
A practical operating model for giving AI complete marketing jobs, setting approval boundaries, and expanding autonomy from evidence.
How to build a commercial content map from your product
Map real product capabilities, buyers, alternatives, objections, workflows, and implementation questions into distinct useful pages.
A practical AI visibility measurement methodology
Choose buyer prompts, sample AI engines, retain source evidence, interpret variation, and measure interventions without false precision.
How to run content gap analysis for B2B SaaS
Find missing B2B SaaS pages across roles, workflows, alternatives, implementation, risk, proof, and AI-answer source gaps.
How to write comparison pages buyers can trust
Research products from current primary sources, compare operating models and fit, disclose limits, and help buyers make a real choice.
How to write alternative pages that help buyers choose
Build alternatives pages around the reason a buyer wants to switch, the real category options, migration cost, tradeoffs, and fit.
An AI content automation quality checklist
Review search intent, originality, evidence, product truth, structure, links, metadata, publication, and maintenance before a page goes live.
How to score PR outreach relevance before sending
Score editorial value, subject fit, recent coverage, contribution, contact confidence, timing, and relationship risk before a pitch.
How to measure an AI marketing agent
Measure opportunity quality, approval, correction, execution, safety, cycle time, outcomes, and supervision cost by marketing job.
How to build an AI content publishing workflow
Design the path from buyer gap and evidence to approved CMS fields, Webflow publication, live URL, measurement, and maintenance.