The NetWebMedia AEO Methodology

Begin with a documented baseline. A heuristic readiness score checks page structure; recommendation checks record actual engine responses for specified buyer questions, dates, language and location. Treat a single response as an observation, not a stable ranking.

What to do:

• Ask 30 high-intent category questions in each AI engine and log which sources get cited
• Identify the top 10 sources being cited (competitors, thought leaders, industry databases)
• Document your current position: Are you cited? How often? What topics trigger citations?

Tool we use:

Claude Projects + spreadsheet logging

How this looks in practice:

Picture a law firm that runs its 30 questions and isn't cited once, while a few competitors show up again and again in "best law firm in [state]" answers. The log shows which sources the engines lean on, and so what to build first: an original, detailed guide to the practice area its buyers ask about most. That guide becomes the first job for Phase 3.

Phase 1 Checklist

Structured data describes facts already visible on a page. Use relevant markup and validate it. Google does not require special schema for AI Overviews or AI Mode. Structured data is not proof of citation or recommendation; effects on other engines remain hypotheses to test.

What to do:

• Add FAQPage schema to your top 10 pages (pull real Q&As from your content)
• Wrap key facts in tables or structured lists so they're easy to extract
• Update Organization markup only with verified facts that also appear in visible content.

Tool we use:

schema.org validator + _add_schema.py script

How this looks in practice:

A restaurant can mark up questions and answers that guests can read on its page. This helps describe the content consistently; it does not establish that an answer engine will cite or recommend the restaurant.

Phase 2 Checklist

Now build the content that AI engines want to cite. Not blog posts. Authoritative, fact-dense, original pieces that answer specific high-intent questions your buyer asks before deciding.

What to do:

• Write one authoritative long-form answer for each gap from Phase 1 (2,500–4,000 words)
• Include primary research: case studies, benchmarks, surveys, anonymized data from your clients
• Update content quarterly. AI engines heavily weight recent refreshes. "Updated April 2026" signals freshness.

Tool we use:

Claude / Anthropic API + approved primary sources + CMS

How this looks in practice:

Picture a boutique hotel publishing "The 2026 Guide to Boutique Hospitality in South America" — 3,200 words, profiles of its own properties, original photography, comparative pricing. A pillar like that is exactly what answer engines cite for "best boutique hotels" queries: original, specific, and verifiable. That's the asset this phase produces.

Phase 3 Checklist

You can't force AI engines to cite you. But you can make it easy for them to discover and understand your content. Distribution happens through search indexing, backlinks, mentions, and freshness signals.

What to do:

• Get indexed: submit sitemap to Google Search Console & Bing Webmaster Tools
• Build backlinks: reach out to related sites, get mentioned in podcasts, speak on industry panels
• Signal freshness: add "Updated [date]" to your key pages. Answer engines favor recent content.

Tool we use:

Google Search Console + Ahrefs/Semrush for backlink tracking

How this looks in practice:

Picture a beauty brand publishing a sunscreen comparison guide, offering it to dermatology associations and industry newsletters, and adding an "Updated [Month Year]" line to the page. None of that forces a citation. It makes the guide easier for answer engines to find, trust, and date, through legitimate distribution rather than paid placement. Phase 5 then shows whether it moved anything.

Phase 4 Checklist

What gets measured gets managed. You need a monthly system to track which questions you're winning, which you're losing, and how your citation rate is trending.

What to do:

• Monthly: Run your 30 target questions through each AI engine again. Log which sources appear.
• Track the metric: % of questions where you're cited. Compare it with your Phase 1 baseline every month and judge progress by the trend.
• Iterate: Double down on topics where you're winning. Fix or replace content where you're losing.

Tool we use:

Spreadsheet + Claude + Kopilot/Semrush for tracking

How this looks in practice:

Picture a plumbing company re-running its 30 questions each month. The log shows it gets cited on emergency-repair questions but rarely on "best plumber in [city]" queries. That gap sets the next piece of content, and the following month's run shows whether it moved the number, measured against the Phase 1 baseline rather than a promised target.

Phase 5 Checklist