PowerUp AI Mastermind — January 8, 2026
First session of the year — symptom-layer GEO, content topology, and the tool consolidation push
“Clients search for symptoms, not root causes. If your content is written for people who already understand what they need, you’re invisible to the people who need you most.” — Lou
This Week in 30 Seconds
- Symptom-layer GEO — Don’s insight that clients search for their pain, not for coaching solutions
- Content topology live demo — Lou walked through the canon → laws → pillars → 24-week plan architecture
- Elizabeth’s GEO monitoring system — automated Perplexity loops to catch when AI stops citing you
- Tool inventory consolidation — Kasimir’s audit surfaced deployable tools that were sitting idle; Lou pushed for N8N as the API backbone
- GEO page structure — question-format titles, direct answer first paragraph, FAQs at the bottom
- Don’s public commitment principle — “throw your hat over the fence” as a forcing function for execution
The Symptom Layer: How Clients Actually Search
The most conceptually rich exchange of the session came from Don Back, and it reframed everything the group has been doing with ontology and GEO work. Don’s observation: clients search for symptoms, not root causes. They type “I feel stuck managing a team that doesn’t take initiative” — not “I need a leadership coach.” They type “I keep undercharging and it’s killing my confidence” — not “I need pricing strategy coaching.”
This is the layer that keyword SEO never reached, and it’s exactly the layer where AI engines operate. When someone asks an AI for help with a felt problem, the AI surfaces content that speaks to that specific symptom. If your content is framed around the solution you offer, AI has no reason to cite you.
Lou reinforced this with the reverse-engineering framing: start from the root cause you understand deeply, then trace backward to every symptom that precedes it. Each symptom is a content opportunity — a question someone is already asking, that you can answer with authority. The group agreed this shifts the content brief radically: you’re no longer writing to explain your methodology, you’re writing to meet a client at the moment of their frustration.
Dirk Ohlmeier noted that this flips the typical coach’s instinct: most coaches write about what they know best. Lou agreed — the shift is to write about what the client knows best first, then bridge to your framework.
“The ontology work is surfacing a beneath-the-keyword layer that regular SEO never reached. That’s the real prize.” — Don Back
Deep Dive: Insight - Map the Symptom Layer to Attract Before You Solve — why AI engines reward symptom-level content and how to build a map of the questions your clients ask before they know they need you.
💡 What This Means for You
List 10 phrases your ideal client says out loud before they Google anything — the complaints, the frustrations, the “I just want to…” statements. Those phrases are your GEO content brief, not your service description.
Content Topology: Building a 24-Week Authority Plan from Canon Outward
Don Back walked the group through the content architecture he’d built over the holiday break — a structured 24-week publishing plan that turns a single canon document into a full authority ecosystem. Lou and Don ran this almost as a joint demonstration, with Lou adding the GEO mechanics layer to Don’s execution framework.
The structure works in layers:
- Canon — the one foundational piece that defines your entire framework; this is your citability anchor, the piece everything else branches from
- Laws — 5–7 core principles derived from the canon; each law becomes a standalone piece
- Pillars — 3–5 topic areas that operationalise each law; these are your content clusters
- Posts — weekly output that drills into specific, searchable questions within each pillar
Lou added the GEO mechanics: the canon document must contain your actual named frameworks, not generic advice. AI engines learn to associate all your branch content with the trunk — but only if the trunk has a unique gravitational pull. Generic expertise creates no anchor. Named frameworks do.
The 24-week timeline reflects the indexing and citation lag Lou had observed in the group’s experiments. “You’re planting the tree,” he said. “By week 20 it should be visible.” Don confirmed he was tracking engagement from week one — not waiting for milestones — so he could see early signals about which content was gaining traction.
Lou also gave guidance on GEO page structure: question-format titles, direct answer in the first paragraph, FAQs at the bottom. This mirrors how AI engines format answers and increases the probability of citation.
Elizabeth asked about how to handle expertise areas where she doesn’t yet have a named framework. Lou: name it now, even if rough. The act of naming creates the citation anchor. Refinement comes after.
Deep Dive: Insight - Use a Content Topology to Sustain GEO Authority Without Drift — how a structured 24-week plan prevents topic scatter and keeps AI engines anchored to your named framework as the authoritative source.
💡 What This Means for You
Take one area of expertise you haven’t fully named yet. Give it a working name — even something provisional. Write one paragraph defining it. That’s the seed of your canon document. Don’t wait until it’s ready.
Elizabeth’s GEO Monitoring System
Elizabeth Stief shared a practical system she’d been running for several weeks: automated daily queries to Perplexity, structured to test whether AI was citing her content on specific topics. This was the most operationally original contribution of the session — turning a vague aspiration (“get cited by AI”) into a measurable feedback loop.
Her approach: she had set up scheduled Perplexity searches for 8–10 questions in her authority area, each designed to mimic what a prospective client would actually ask an AI engine. She tracked responses weekly, noting when her content appeared, when it dropped out, and when competitor content appeared instead.
The key insight from her experience: most people running GEO strategies have no systematic way to know whether it’s working. The monitoring system creates the feedback loop. Without it, you’re publishing into a void and guessing at results.
She also built her guidance document differently from most: she fed each AI model information about what that model looks for when evaluating content quality, then used Perplexity’s scheduled task feature to send monthly updates — keeping the guidance current as model behaviour evolved.
The group discussed automating this further with N8N. Don Back suggested connecting Perplexity query outputs to a tracking spreadsheet automatically. Lou confirmed N8N could handle this: Perplexity node → Google Sheets node, running on a daily schedule. What Elizabeth was doing manually could be fully automated.
💡 What This Means for You
Pick 3 questions your ideal client would ask an AI engine about your expertise area. Run them in Perplexity today. Save the results. Run them again in 4 weeks. The delta is your GEO signal.
Go Deeper:
- Perplexity AI — used for scheduled GEO monitoring queries (perplexity.ai)
Kasimir’s Tool Inventory Audit
Kasimir Hedstrom had spent part of the holiday break doing something the group immediately recognised as overdue: a systematic audit of every AI tool he had built or subscribed to. The audit surfaced several tools he’d built with Claude that were production-ready but sitting idle — never deployed, never used downstream.
Kasimir’s framework: he used Claude’s cross-conversation memory capability to reconstruct a complete inventory of tools he’d built across multiple sessions. This surfaced finished tools he’d forgotten about. The audit question he applied to each: is this deployed and in use, or is it just code sitting in a folder?
Lou extended this into a broader recommendation for the group: stop fragmenting AI tool use across 10+ separate subscriptions. Consolidate into a unified API infrastructure — his recommendation was N8N — that can route between models, handle logic, and connect tools without manual transfer steps. The goal: one orchestration layer, multiple specialised tools behind it, accessible by API rather than by switching between chat interfaces.
He flagged that the consolidation work itself is an AI task: feed Claude your tool inventory and ask it to identify overlaps, redundancies, and consolidation paths.
💡 What This Means for You
Do a 15-minute tool audit this week. List every AI subscription and every AI tool you’ve built. Ask: is this used weekly? Does it feed into something else? Cancel or pause anything that fails both questions.
Go Deeper:
- N8N — open-source AI workflow automation (n8n.io)
Community Corner
Don Back started the year with double-leverage thinking. He’d realised over the break that his website rebuild and his GEARS intake asset preparation were essentially the same work — and was running them in parallel rather than sequentially. Lou called this out as the kind of systems thinking that separates productive entrepreneurs from busy ones: two projects, one workstream, double the output.
Don also shared his “throw your hat over the fence” principle — committing publicly to a project as a forcing function for execution. His observation: private planning allows indefinite delay; public commitment accelerates learning, development, and action in ways that internal deadlines don’t.
Dirk Ohlmeier raised a question about German-language GEO — whether strategies developed for English content would translate to German-language markets where AI engine behaviour and citation patterns might differ. Lou acknowledged this as genuinely uncharted territory and encouraged Dirk to document his findings. The group agreed: if most GEO authority research is in English, being an early mover in German-language GEO could yield disproportionate results.
Donald Kihenja shared a useful implementation note: you can create scoring and evaluation logic in an LLM and host it in GoHighLevel (GHL) as custom code — faster deployment, all in one place. He offered to share his implementation with anyone interested.
Links Shared in Chat
- N8N — AI workflow automation backbone (n8n.io)
- Perplexity AI — AI search used for GEO monitoring (perplexity.ai)
- Amy Yamada’s website — shared by Coach Bally Binning (amyyamada.com)
Try This Before Next Session
Run a symptom-layer audit on your current website or main service page. This takes 15–20 minutes and will immediately reveal the gap between how you describe your work and how your clients actually experience their problem.
- Open your homepage or main service page.
- List every heading, subheading, and opening sentence.
- For each one: is this written for someone who already knows they need coaching, or for someone searching for a solution to a felt problem?
- Flag every line that assumes the reader already understands what you offer.
- Rewrite one flagged line using symptom-first language — what the client feels, not what you solve.
Bring your before/after to next session.
Open Threads
- How do you prevent content drift in long AI-assisted content programs without making the AI repetitive?
- At what point does engagement tracking need to be automated, rather than done manually?
- How do you write content that serves GEO discoverability and human conversion simultaneously without the tone of one undermining the other?
- How do you convert an inventory of AI-built tools into a productised, API-accessible suite?
- What does GEO look like in non-English language markets — does the same authority architecture apply?
Next session: 2026-01-15
Derived Artifacts
- geo-page-audit (GEO Page Audit — GEARS and AI-optimized schema)