Program Guide: Get Found in AI Search
Your team is running an AI-visibility program using ai12z. This guide is how you prepare, run the working session, and keep it going.
Every page in this guide assumes you're building this program for your own organization — not for a client, and not as a one-time ai12z demo. It gives you the methodology, the real report data to understand what "good" looks like, the exact ai12z screens to use, and the questions your own stakeholders are likely to ask, organized around a single operating model: Measure → Improve → Prove.
Who This Is For
Marketing, content, SEO, and growth teams running their own AI-visibility measurement and improvement program on the ai12z platform. If you're looking for ai12z's own reference documentation on what each report does, start at the GEO Suite Overview instead; this guide is about how to run that suite as an ongoing internal program.
No prior GEO/AEO experience is assumed. This guide works whether your team has never heard of Generative Engine Optimization or already tracks industry coverage of it weekly.
The Operating Model: Measure → Improve → Prove
| Verb | What It Means | What It Looks Like |
|---|---|---|
| Measure | Establish a real, numbers-backed baseline — not an opinion about how visible the brand "feels" | A Citation Monitor health score, a Q&A Analysis GEO score, an Agent-Readiness score |
| Improve | Fix the highest-leverage, most concrete gaps first — not everything at once | Publishing a generated llms.txt, adding FAQPage schema, fixing a pricing inconsistency, filling a comparison-content gap |
| Prove | Re-run the same saved list or report and show the delta | Re-running a Citation List after a fix; a GEO Trend Comparison between two periods |
This is the same loop The ai12z Advantage describes at the platform level — your program is that loop, run once to build a baseline, then repeated on a cadence your team owns.
What Your Team Gets Out of This
Four concrete outcomes — worth stating explicitly when you pitch this internally, because they're what you're actually building:
- A working understanding of how AI answer engines discover, cite, and recommend brands — specific to your industry, not a generic slide.
- Your organization's own highest-value content, brand, and technical gaps — not a checklist, an actual prioritized list from Your Working Session.
- A saved measurement baseline — a Citation List and/or Keyword List that didn't exist before, ready to re-run next month.
- A prioritized 30-day roadmap — ranked, effort-tagged, modeled on The 30-Day Roadmap.
Setting Expectations Honestly
Two things worth saying to your own stakeholders before you start, because unmanaged expectations are the fastest way to lose internal support for a new program:
- The baseline you build will not be flattering. Every real example used throughout this guide — a Citation Monitor "Emerging" tier score of 32/100, a Site-Wide Sweep finding that 49% of pages have images missing dimensions, an AI Footprint finding of zero Wikipedia presence — came from a real, otherwise-healthy company. A weak first baseline is the normal starting point, not a sign your marketing team has failed.
- The working session produces a plan, not finished work. It ends with a prioritized list and a scheduled re-test date, not a rebuilt website. See The 30-Day Roadmap for what actually happens between sessions.
Format at a Glance
| Cadence | Baseline once, then monthly re-testing after that |
| Prep time | Reports run asynchronously before the session — see Getting Ready |
| Working session length | 60–75 minutes — see The Agenda |
| Format | A working session interpreting a baseline you've already built, not a from-scratch build in the room |
| Prerequisite | Where your organization is starting from — see Where You're Starting From — determines what you can prepare beforehand |
| Output | A citation baseline, a first Citation List and/or Keyword List, a categorized gap list, and a prioritized 30-day roadmap with a scheduled re-test date |
What Makes This Credible
This guide isn't built on hypothetical best practices. Every exercise runs against the same reports documented in the GEO Suite Overview, and every example in this guide is drawn from real report output — real ai12z.com data, the same data referenced throughout the reference pages. When this guide says "a real Citation Monitor run showed a competitor beating the brand with 19% share of voice on three uncited category queries," that's not a hypothetical — it's the literal example used in Citation Monitor, and it's exactly the kind of finding you'll surface for your own brand.
Guide Chapters
| # | Chapter | What It Covers |
|---|---|---|
| 1 | Program Guide Overview (this page) | The operating model, what your team gets, format at a glance |
| 2 | Why This Matters Now | The discovery shift from links to answers; SEO vs. GEO/AEO |
| 3 | The ai12z Advantage | Why ai12z's chatbot-layer ownership makes this a loop, not a one-time audit |
| 4 | Where You're Starting From | What you can measure today vs. what unlocks as you accumulate usage history |
| 5 | Getting Ready | Stakeholder alignment, required access, which reports to run first, and a deliverable template |
| 6 | The Agenda | The working session's run of show, and what happens before it |
| 7 | What You'll Measure | The four measurement pillars |
| 8 | Your Working Session | Interpreting the prepared baseline with your team and building the roadmap |
| 9 | Optimization Playbook | The five fix categories, each with a real before/after example |
| 10 | The ai12z Toolbox | Every report used across this guide, and when to reach for each |
| 11 | The 30-Day Roadmap | Week-by-week plan your team takes home |
| 12 | Building an Internal Program | Making this a recurring practice — ownership, cadence, and when to bring in outside help |
| 13 | Close & Next Steps | Wrap-up checklist and what happens next |
Related Documentation
- Get Found in AI Search: Customer Program — Back to the program landing page
- GEO Suite Overview — The full reference documentation for every report this guide uses
- Consolidated Action Plan — Where the working session's output ends up afterward