The full audit lives in the companion document. If you're reading this in isolation, here is the version that lets you make a decision about scope.
8.6% recognition across both names. AI is currently routing your customers to Publishing.com.
This is fixable. AI models cite a small, specific set of venues when answering questions in your category — the audit's citation map is the inventory. Where you're present and well-represented in those venues, AI gets you right. The rest of this document is what closing that gap looks like in practice.
The shape is mostly a citation-density problem, not a discoverability one. Your moat in AI search isn't "we rank #1" — it's "every venue AI cites about our category mentions us correctly." That's a structural fix, and it follows a known sequence.
Resolve entity ambiguity and anchor the Nespola / PublishingOS relationship across the structured-data layer. Founder LinkedIn anchored. Trustpilot claimed. Organisation & Person schema deployed. The plumbing AI search reads from.
Build presence where AI looks for evidence in your category — Reddit, YouTube, Trustpilot, KDP forums. The Nespola vs Publishing.com comparison page goes live. We publish articles answering the questions AI is currently getting wrong. We start posting in r/KDP and r/selfpublish on a regular schedule.
The work shifts from setup to monitoring. We re-scan against the audit baseline regularly, watch which prompts and platforms are moving, and adapt the next month's focus to what's actually shifting. AI training cycles are uneven and outside our control — we don't promise a specific recognition number by week 12. What we promise is that you see what's changing in real time, and that the strategy responds to it.
Backlinks and articles still matter, but they're a fraction of the surface area. AI visibility is a different discipline from traditional SEO — different surfaces, different feedback cycles, different failure modes. Most of the work AI search responds to lives at layers traditional SEO doesn't touch.
The layers must be sequenced. Citation density built on top of an unresolved entity graph reinforces the wrong relationships in AI memory. Counter-misinformation deployed without measurement is invisible. Each layer depends on the one below it being right first.
AI models update training and retrieval signals on cycles that range from continuous-but-throttled (Perplexity, ChatGPT search) to quarterly retraining (Gemini, Claude). The 90-day shape isn't padding — it's the realistic cycle in which AI search starts noticing the work, regardless of how fast we move.
Three predictable failure modes when brands attempt this work without specialist support:
AI search visibility is a citation-density problem that splits cleanly into a technical side and a content / SEO authority side — and the work compounds when both move in lockstep. SEO and AI search are not separate problems; they share the same evidence base. Fixing one feeds the other.
Founder of Node AI. Built and runs the platform used for the audit.
The technical work behind it (schema, entity engineering, founder anchoring) is what makes AI engines confident enough to surface a brand over its competitors when buyers ask. Without that layer, content alone doesn't break through. With it, you become the answer.
He built this tool, which means he understands AI visibility's technical side from the inside out and sees what actually moves citation rates.
10+ years in copywriting and SEO, with clients including Coca-Cola HBC and other enterprise brands.
Across client engagements, 90%+ of targeted keywords landed on Google's first page through long-form content and website copy. Work spans case studies, LinkedIn ghostwriting, and content strategy across B2B tech verticals.
Now applying that same content foundation to GEO by building content that LLMs cite when buyers ask who to trust.
One entry point, two ways forward. Foundation Build is a 30-day project to ship the technical and authority foundation; Advisory and Full Partnership are monthly retainers, the first for teams with writers in-house, the second for an embedded delivery team.
We build the AI visibility foundation and hand it back. Right if you have the team to run it from there but want the technical and authority groundwork shipped properly first.
€3,000 credits to your first month if you upgrade to a retainer within 60 days. Fix-or-refund: if we don't ship the scoped foundation within 30 days, full refund.
We own the technical, authority, and strategy layer. Your team owns the writing and the founder-led outreach. Right if you have writers and content capacity in-house and want strategic direction without giving up content production.
An embedded AI search and SEO team running continuously while you focus on Skool, the agency arm, and Nespola itself. Each month's focus shifts based on what the data surfaces as highest-leverage.
Most engagements follow this shape. The actual plan adapts month to month based on what the dashboard surfaces.
Strategy doc: citation-source map of Publishing.com / Mikkelsen Twins (which channels, formats, and topics earn their AI mentions); nespola.io technical audit; community-engagement audit; content gap analysis.
Six-month operating plan: month-by-month content plan with target keywords and the AI prompts each piece is built to win; outreach target list and templates; community-engagement playbook.
Three live entity assets shipped in Month 1: founder LinkedIn profiles fully anchored, Trustpilot profile claimed, Organization & Person schema deployed. Foundational moves that don't need diagnosis and start working immediately.
The priority piece: Nespola vs Publishing.com comparison page. Highest-intent, highest-converting page we can build. AI is already failing on this exact prompt today.
Full technical foundation deployed: indexing, page speed, on-page SEO; full schema markup (Course/Product, FAQ on top of Phase 1); Trustpilot review-request flow live.
First batch of cornerstone content live targeting the prompts AI is already mis-answering. Community and LinkedIn cadence begins (2x/week posting alternating between Tommi & Nic).
6 pieces of content per month in steady state. Mix of comparison cornerstones, AI-assisted topic-cluster pieces, and supporting articles.
Founder LinkedIn keeps building: 8 ghostwritten posts per month combined (2x/week alternating between Tommi & Nic).
Network-dependent work starts landing: guest features, podcast appearances, listicle inclusions, citation lift across a wider prompt set. Pace varies, no monthly guarantee. This is when the relationships and outreach playbook from Phase 1 start to compound.