The Web3 AI Visibility Audit: A 12-Step GEO Checklist
A practical 12-step audit for finding where AI engines misunderstand or overlook your Web3 project. Covers branded prompts, category prompts, competitor comparisons, crawler access, documentation, citations, sentiment, and content gaps.
Joha J. Sulaymonov
Co-CEO, Lanesra AI
The short answer
Most Web3 teams have never audited their AI visibility because there is no obvious place to start. There is no equivalent of Search Console for generative engines, no dashboard that tells you your project is being described incorrectly in two of three major models.
This is the audit. Twelve steps, ordered so that each one informs the next. You can run the whole thing manually in a focused day or two, and you should, at least once, because doing it by hand is how you develop intuition for what the failure modes actually look like. After that, the manual version is unsustainable, and the last section covers why.
The Four Phases
STEPS 1-3
Measure
What do engines say about you right now, and who occupies your slots
STEPS 4-6
Readability
Can machines access and understand your own surfaces
STEPS 7-9
Evidence
Does independent corroboration exist, and is it any good
STEPS 10-12
Coherence
Is your story consistent, and where are the holes
The order matters. Running step 12 before step 2 produces a content plan disconnected from actual prompt demand. Fixing documentation before checking crawler access means fixing content nothing can read. Work forward.
The 12 Steps
Branded prompt sweep
Ask every engine directly about your project by name. Run at least five phrasings: what is X, is X legitimate, who built X, is X safe to use, what does X do. Record the exact description each engine returns, verbatim.
PASS: every engine returns a description that is accurate and current.
FAIL: any engine returns a wrong category, a superseded architecture, or 'I have no information about'.
Continuous in Lanesra: Verbatim engine responses per branded prompt, tracked over time so drift is visible
Category prompt sweep
Now remove your name. Ask the questions a prospective user asks before they know you exist: best L2 for X, safest protocol for Y, which chain should I build Z on. Twenty to forty prompts minimum, covering your real use cases.
PASS: you appear in a meaningful share of category prompts.
FAIL: you appear in none, which is the most common result and the reason this step exists.
Continuous in Lanesra: Prompt-level visibility across your full category set, not a single aggregate score
Competitor comparison mapping
For every category prompt where you do not appear, record who does. Build the list of names occupying your slots, and note their position in the ranked answer.
PASS: you understand exactly which projects hold the slots you want.
FAIL: you have a visibility number but no idea who is beating you.
Continuous in Lanesra: Competitor ranking positions per prompt per engine, side by side with yours
Crawler accessibility check
Fetch your own key pages as a bot would: no JavaScript, no cookies, no login. Check robots.txt for accidental blocks on AI crawlers specifically. Verify your docs render server-side.
PASS: all substantive content is readable without a browser session.
FAIL: your best technical explanation requires JS rendering, or robots.txt blocks GPTBot and similar agents.
Continuous in Lanesra: Which of your domains and pages actually appear as cited sources versus never surfacing
Structured data review
Check schema markup on your key pages: Organization, SoftwareApplication, FAQPage where relevant. Verify heading hierarchy is logical and that definitional sentences exist near the top of each page.
PASS: clean schema, logical headings, extractable definitions.
FAIL: no schema, heading soup, or your core definition buried in paragraph six.
Continuous in Lanesra: Which specific pages get cited versus merely indexed, indicating where extraction works
Documentation completeness
Read your own docs as a stranger. Can someone determine your architecture, chain, security model, and primary use case without prior context? Is there a single canonical page that states what you are?
PASS: a stranger can categorize you correctly in under two minutes.
FAIL: your docs assume the reader already knows what you do.
Continuous in Lanesra: How each engine categorizes your project, which reveals whether your docs are doing their job
External citation inventory
List every independent source that describes your project: audits, aggregator listings, media coverage, research writeups, directory entries. Count them. Check each for accuracy.
PASS: multiple independent, accurate sources exist.
FAIL: the only substantive description of your project lives on your own domain.
Continuous in Lanesra: Which external domains engines cite in your category, and whether you appear in them
Sentiment and framing check
Look at how you are described qualitatively, not just whether you appear. Note hedging language, unresolved criticism, or references to past incidents that appear without context.
PASS: descriptions are neutral to positive and factually framed.
FAIL: engines attach caveats, reference old incidents, or hedge on legitimacy.
Continuous in Lanesra: Sentiment scoring per engine alongside visibility, so tone problems are separable from absence
Source authority assessment
Of the sources that do mention you, weight them. An audit from an established firm and a DefiLlama listing carry more weight than a low-quality directory or a paid listicle.
PASS: your mentions cluster in high-authority sources.
FAIL: your mentions cluster in SEO farms and paid placements.
Continuous in Lanesra: Which sources engines actually pull from when constructing answers about your category
Messaging consistency sweep
Compare your positioning across every surface: homepage, docs, GitHub README, governance forum, aggregator listings, old blog posts, third-party directories. Note every place the description diverges.
PASS: one consistent description everywhere.
FAIL: three or more materially different descriptions live simultaneously, which is extremely common.
Continuous in Lanesra: Whether different engines return materially different descriptions, the clearest symptom of source conflict
Social and community identity check
Verify that your official accounts are clearly identifiable and consistent. Check for impersonation accounts, abandoned handles, and community channels carrying outdated positioning.
PASS: official identity is unambiguous across platforms.
FAIL: abandoned or duplicate accounts create entity confusion for the model.
Continuous in Lanesra: Mentions tracked regardless of authorship, including community and third-party sources
Content gap analysis
Compare the prompts where competitors appear and you do not against your existing content. Identify which questions you have simply never publicly answered.
PASS: you have a prioritized list of content that maps to real prompt demand.
FAIL: you are writing content with no reference to what people actually ask AI.
Continuous in Lanesra: Prompt demand data mapped against your current visibility, producing the gap list directly
Scoring the Audit
Count your failures across the twelve steps. This is crude, and it is deliberately crude, because the alternative is a weighted composite that hides which specific thing is broken.
| Failures | Status | What it means |
|---|---|---|
| 0-2 | Healthy | Move to continuous monitoring. Your problem is maintaining position, not establishing it. |
| 3-5 | Fixable | Specific, addressable gaps. Prioritize any Tier 1 failures (steps 1, 2, 4, 7) first. |
| 6-8 | Structural | Multiple systems are broken at once. Fix entity clarity and crawler access before touching content. |
| 9-12 | Rebuild | Treat AI visibility as a new workstream, not a marketing task. Start from canonical positioning. |
One caution on interpretation: a low failure count on a project nobody asks AI about is not a win. Steps 1 through 3 are the ones that tell you whether there is demand to compete for in the first place. If your category prompts return almost nothing meaningful across all engines, the visibility problem may be downstream of a category awareness problem, which no amount of GEO work fixes.
Why the Manual Version Stops Working
Run this audit once and you have a baseline. That baseline is accurate for roughly as long as nothing changes, which in Web3 is not long.
Three things break a manual audit. First, engine responses are non-deterministic: ask the same question twice and phrasing shifts, sometimes substantively. A single manual pass is one sample, and one sample cannot distinguish a real absence from variance. Second, models update. A description that was accurate in March can drift by June with no action on your part. Third, competitors move. The ranked list you recorded in step 3 is a snapshot of a contest that continues after you close the spreadsheet.
The practical consequence is that the manual audit tells you where you stand on one day, and the thing you actually need to know is the direction of travel.
Recommended cadence once instrumented
Running It Continuously
Steps 1, 2, 3, 7, 8, 9, and 12 are the ones that need instrumentation, because they measure external output that changes without your involvement. This is what Lanesra is built to run: branded and category prompt tracking across ChatGPT, Gemini, Google AI Overviews, AI Mode, Perplexity, and Copilot, competitor position per prompt, citation versus mention breakdown, sentiment per engine, and the gap list that falls out of comparing prompt demand against your current visibility.
Steps 4, 5, 6, 10, and 11 stay partly manual, and should. Crawler configuration, schema markup, documentation quality, and account hygiene are your own surfaces, and fixing them is engineering and editorial work rather than a monitoring problem. A quarterly manual pass on those five is sufficient.
The value of splitting it this way is that you stop confusing two different kinds of problem. External visibility drift needs continuous measurement because it moves on its own. Internal readability needs periodic review because it only moves when you change something. Treating both as the same task is why most audits get run once and never again.
Run the audit continuously
Branded prompts, category prompts, competitor positions, and citation quality, tracked across every major engine.