Content systems built to rank in Google and get cited by AI
Technical SEO, information architecture and editorial workflow for multi-region B2B and fintech funnels. Every article is built on keyword data from the top 10–30 ranking pages and carries a content score from blank page to publish. GEO, generative engine optimization, is the same discipline aimed at AI answers.
Intent first,
then architecture
01 · Map intent & markets
I start from what the SERP already rewards. Keyword data mined from the top 10–30 ranking pages per term tells me what a page has to cover before anyone writes a sentence.
In practice that's fintech keyword research run through NeuronWriter, sorted into intent tiers per market. I build one topic map, then split regional variants for NA, EU and SEA instead of translating a single page three times.
The brief describes what already ranks02 · Architect the site
I treat crawlability as infrastructure: if engines can't read the site cleanly, no amount of content fixes it. So the architecture gets fixed before the editorial calendar gets filled.
That means sitemaps, schema, canonicals, hreflang and Core Web Vitals worked with engineering across a multi-region site. It also means a crawler-access audit, since roughly 6% of sites block AI crawlers in robots.txt without realizing it. I rebuilt an international site on Webflow for exactly this: speed, control and SEO under one governed CMS.
If engines can't read the site, nothing else matters03 · Produce on a scoring system
I give writers a number to hit. Every article starts at a content score of 0, and the workflow moves it up.
A data-built outline takes the score to 40–50 before a sentence of prose exists. The full ~1,500-word draft lands in the mid-70s or 80s, with ChatGPT in the workflow for structure and metadata while a human makes the final call. Localization runs on the same briefs, so three regions ship from one calendar.
Writers hit numbers04 · Optimize for both engines
I close the loop twice, because there are two engines now. GA4, Search Console and cohort attribution cover classic search; for AI search I watch whether the content gets cited at all.
The levers differ. In AI answers, mentions, structural clarity and freshness beat backlinks, and those engines trigger far more on specific 7+ word questions than on head terms. So I restructure pages to answer those questions directly and refresh them so they stay worth citing.
Rank in Google, get cited by AI, same contentOrganic systems I have built across three regions:
Research & intent
Technical SEO & IA
Content systems
GEO / AI search
What I research and score with:
crawl, keyword and editorial tools
scoring
Ahrefs
SEMrush
NeuronWriter
Google Search Console
& site
Screaming Frog
⚡Core Web Vitals
Webflow
Hotjar
system
& AI
GA4
◔Cohort attribution
ChatGPT in the workflow
A scoring system that made coverage measurable before publish
Every article starts at a content score of 0. A data-built outline takes it to 40–50 before a sentence of prose exists, using keyword data mined from the top 10–30 ranking pages. The full ~1,500-word draft lands in the mid-70s or 80s. Those numbers are process telemetry, not rankings; rankings are a separate measurement. What the score buys is that every article walks into the SERP with the coverage the top pages already have. Writers hit numbers, and the same discipline rebuilt an international site on Webflow for speed, control and SEO.
Read the full case ›Daniel is a standout professional who brings a wealth of knowledge and positivity to the workplace. He is exceptionally kind, funny, and a collaborative team member.
Ginger SumnerSenior Project Manager
Need an organic engine that outlives the next update?
One article, written once, has to satisfy a crawler and an answer engine.