Perplexity Citation Audit: Check Visibility and Crawl Access
How to check whether Perplexity actually surfaces your brand — and why the audit has to include raw server logs, not just prompt sampling.
nqzaiBlogSeries archive
How to check whether Perplexity actually surfaces your brand — and why the audit has to include raw server logs, not just prompt sampling.
How to track a brand across Gemini, AI Overviews, and AI Mode — and why Google-Extended, grounding, and the Knowledge Graph aren't the same lever.
Microsoft Copilot leans on Bing's index, not an independent crawl. Here is what B2B teams can now actually measure, track, and what still cannot be proven.
A four-section monthly template for AI-search visibility reporting — observed answers, referral data, shipped changes, and an honest unknowns section.
A practical do/don't framework for AI-assisted conversion claims — what GA4 referral, landing-page, and multi-touch data actually supports, and what goes beyond it.
A framework for tracking which claims cite which sources, catching link rot and source drift before it erodes AI-search trust.
A byline isn't a review. Here's how to build a real subject-matter-expert checkpoint into your pipeline before AI engines cite your claims.
A content-brief format built for answer engines: start from real user questions, then map every claim to its evidence before writing begins.
A repeatable claim-evidence-source-date template that makes B2B product and outcome claims verifiable — and citable — by AI answer engines.
How to build one canonical facts page AI answer engines can trust to resolve stale or conflicting claims about your company.
Product specs, pricing, and comparison tables are often invisible to AI crawlers. Here's the markup that lets them parse and cite your data correctly.
Canonical tags tell Google which duplicate wins. AI crawlers don't confirm they even read the tag — here's what actually shapes citations.
ChatGPT, Perplexity, and Claude don't render JavaScript the way Googlebot does. Here's what that means for content teams.
Google says schema isn't a ranking or AI-citation lever. Here's the real evidence on what structured data does — and doesn't — do for AI search visibility.
Google restricted FAQ rich results to gov/health sites in 2023, then killed them entirely in 2026. Here's what FAQPage schema still does for AI search.
How to structure Product and Offer schema.org markup so AI shopping agents and Google read price, stock, and specs without guessing.
llms.txt isn't a one-time SEO file. Here's how to govern it on an ongoing basis — and what the adoption data actually shows in 2026.
A realistic 90-day AEO roadmap for B2B SaaS: entity clarity, evidence pages, technical crawlability, answer-led content, measurement, and what to prioritize first.
How ecommerce brands earn citations in ChatGPT Shopping, Google AI Mode, and Perplexity — and how to prove it's driving revenue, not just visibility.
A repeatable framework for agencies selling AEO/GEO: client discovery, evidence mapping, technical QA, reporting cadence, scope controls, and honest limitation-setting.
A practical operating framework for tracking rank, AI citations, and answer-engine visibility separately — with real data sources, cadence, and decision rules for each.
A practical RFP template for vetting AEO/GEO agencies — methodology, evidence standards, measurement, technical scope, and how honestly they discuss limits.
A practical framework for B2B leaders choosing between building AEO/GEO capability in-house or hiring an agency — capability, data, ownership, speed, cost.
A practical checklist for AEO/GEO deliverables: technical audits, evidence-based content plans, QA operations, honest measurement, and the limits no agency can promise away.