TL;DR
AI Rank Lab and Stobo take fundamentally different approaches to AEO auditing. AI Rank Lab markets "100+ factors" (actually around 20 distinct checks) with broad surface coverage. Stobo focuses on 7 research-backed checks with granular depth. Their pricing model advertises "free scan" but requires $2.49 to view results. Stobo's free tier is actually free with full results immediately visible.
We tested our own site with their tool and found scoring inconsistencies:
- llms.txt showed three different values (0, 10/10, and "Missing") in the same report
- FAQ presence scored 0 while faqSchema scored 15/15 perfect
- Brand mentions showed 8/8 in one section, "Missing" in another
Stobo's single-score approach with transparent methodology avoids this confusion. We focus on technical barriers that block AI citations rather than maximizing check count for marketing. Different philosophies for different use cases.
AI Rank Lab is an established player in the AEO space. We tested our own site with their tool to understand their approach and compare methodologies honestly.
The GEO market is projected to grow from $848 million to $33.68 billion by 2034. Tools are multiplying fast. Understanding what each one actually measures helps you choose the right fit for your needs.
What AI Rank Lab actually measures
Their marketing emphasizes "100+ factors." The reality is approximately 20 distinct checks. The 100+ number represents their internal point system, not unique factors analyzed.
We went through their audit report and identified these checks:
Technical foundation (26 points total)
- robots.txt configuration (3 points)
- XML sitemaps (5 points)
- HTTPS security (5 points)
- Mobile-first design (5 points)
- Semantic HTML (5 points)
- ARIA labels (3 points)
Content structure (56 points total)
- llms.txt file (10 points)
- FAQ schema (15 points)
- Direct answer formatting (18 points)
- Header structure (5 points)
- Lists and tables (5 points)
- Citations (3 points)
Content quality (21 points total)
- Content freshness (5 points)
- Content depth/word count (8 points)
- E-E-A-T signals (8 points)
AI optimization (41 points total)
- NLP optimization (8 points)
- Topical authority (10 points)
- Entity recognition (8 points)
- Voice search readiness (5 points)
- Zero-click optimization (10 points)
Total: approximately 144-180 points depending on how subchecks are weighted.
Many of these are legitimate AEO factors. Research shows 61% of pages cited by AI use three or more schema types, so checking FAQ schema and structured data makes sense. Some checks are basic SEO (HTTPS, mobile-first) repackaged as AEO. The breadth is genuine, even if the "100+" claim inflates perception.
The problem isn't what they check. It's how the scores behave.
What we tested
We ran trystobo.com through AI Rank Lab's full audit. Here's what their system reported:
Eight different scores for one website:
| Score Type | Result |
|---|---|
| Overall Score | 56 |
| AEO Score | 40 |
| AI AEO Score | 75 |
| GEO Score | 71 |
| AI GEO Score | 50 |
| Technical | 85 |
| SEO Score | 73/120 |
| AEO/GEO Score | 33/100 |
The relationship between these scores is unclear. Why does "AEO Score" (40) differ from "AI AEO Score" (75) by 35 points when both claim to measure the same thing?
Checks that worked correctly:
- robots.txt: 3/3
- xmlSitemaps: 5/5
- freshness: 0/5 (accurate, we hadn't updated recently)
The technical checks function. The scoring logic doesn't.
The contradictions we found
The most concerning discovery was scoring inconsistency within the same report. Not across different audits. Within a single report, the same check shows different results.
Summary of contradictions:
| Check | Value 1 | Value 2 | Value 3 |
|---|---|---|---|
| llms.txt | 0/10 | 10/10 (perfect) | "Missing" |
| FAQ | 15/15 (perfect) | 0 | "Missing" |
| Brand mentions | 8/8 (perfect) | "Missing" | — |
| Direct answer | 0/18 | Correctly extracted | — |
llms.txt: three values for one check
Our llms.txt file exists at trystobo.com/llms.txt. It's valid, properly structured, follows the specification. The report showed 0 in the GEO Summary, 10/10 in the Detailed Breakdown, and "Missing" in the AI Optimization section.
Which score reflects reality? All three appear in the same report for the same website at the same time.
FAQ content: perfect and missing simultaneously
We have FAQ content with proper FAQPage schema. Research shows FAQ-optimized content achieves 41% AI citation rates versus 15% for unstructured content. Getting this check right matters.
The schema check found it (15/15 perfect). The FAQ presence check scored it zero. The FAQ Content section marked it "Missing." The same content can't simultaneously exist perfectly and not exist at all.
Brand mentions: conflicting data
The Visibility breakdown showed 8/8 (perfect). The AI Optimization section showed "Missing." Same report, same website, opposite conclusions.
Direct answer: extraction worked, scoring didn't
Score: 0/18. But their report correctly extracted and displayed our direct answer text as evidence they parsed it.
They found it. They showed it. They scored it zero.
We're reporting these findings factually. These might be technical bugs in their scoring engine rather than intentional design. But if you're making decisions based on these scores, the inconsistencies matter regardless of cause.
Where the approaches differ
| Factor | AI Rank Lab | Stobo |
|---|---|---|
| Check count | ~20 checks (marketed as "100+ factors") | 7 checks |
| Score presentation | 8 different scores per report | Single score (0-100) with letter grade |
| Focus | Broad coverage including basic SEO | Technical barriers that block AI citations |
| Free tier | Requires $2.49 payment to view results | Full results immediately, no payment |
| Report access | URLs expire, session-locked | Shareable, no expiration |
| Methodology | Scoring logic not publicly documented | Published with research citations |
The philosophical difference: breadth versus depth.
AI Rank Lab covers more ground. If you want a checklist that touches HTTPS, mobile-first design, ARIA labels, and voice search readiness alongside AEO factors, their approach provides that surface scan.
Stobo goes deeper on fewer checks. We focus on the technical barriers that actually block AI citations: robots.txt configuration for 21 specific AI crawlers, llms.txt validation against the specification, FAQ-schema pairing analysis, and content structure optimization. Research shows 87% of pages cited by AI use a single H1 as the primary anchor. We check that. We don't check whether you have ARIA labels.
The $2.49 "free scan" is worth noting. Their marketing says free. The results are paywalled. We show everything immediately because the audit is the top of our funnel, not a revenue gate.
Pricing comparison
| Factor | AI Rank Lab | Stobo |
|---|---|---|
| Free tier | $2.49 to view results | $0 (full audit, no paywall) |
| What free includes | Score summary only | All 7 checks with details |
| Premium | Undisclosed enterprise pricing | $199 one-time |
| What premium includes | Unknown | Action plan, llms.txt generation, implementation code |
| Report access | Expires, returns 404 | Permanent, shareable |
| Unlimited runs | Unknown | Yes |
Different business models, different incentives.
AI Rank Lab uses the scan as a monetization point. You pay to see what's wrong. This creates pressure to show problems worth $2.49, whether or not they're the problems that matter most.
Stobo uses the scan as a discovery tool. You see everything for free. We monetize the solution, not the diagnosis. Our incentive is accuracy: if the audit shows false problems, you won't trust the action plan enough to buy it.
The $199 premium tier includes implementation-ready code. Not "here's what's wrong" but "here's the robots.txt rules, the llms.txt file, the schema markup, copy and paste." The GEO market is projected to reach $33.68 billion by 2034. We're betting that founders will pay for fixes they can ship in an afternoon, not dashboards they need to interpret.
When to use each tool
AI Rank Lab makes sense if you:
- Want surface coverage across ~20 factors in one scan
- Need checks Stobo doesn't offer (E-E-A-T signals, entity recognition, Knowledge Graph optimization, voice search readiness)
- Don't mind reconciling 8 different scores to understand your position
- Can verify contradictory results manually before acting on them
Stobo makes sense if you:
- Need focused analysis on technical barriers that actually block AI citations
- Want one score, one grade, one clear answer
- Value transparent methodology with research citations
- Need implementation-ready code you can ship today
- Want permanent, shareable reports
The honest take
AI Rank Lab covers more ground. If their scoring inconsistencies get fixed, the breadth would be genuinely useful. Right now, you'd need to verify each check manually before trusting it.
Stobo covers less ground, but what we check is validated. As the Graphite CEO put it: "Early-stage startups can win at AEO when they couldn't at SEO." We built for that use case. Seven checks, deeply analyzed, with code you can copy and paste.
If budget allows both, run AI Rank Lab for the checklist, then run Stobo for the implementation plan. Just don't make decisions based on scores that contradict themselves.
What they do better than us
Credit where it's due. AI Rank Lab has legitimate strengths.
Broader factor coverage. They check E-E-A-T signals, entity recognition, knowledge graph optimization, and topical authority. These are legitimate AEO factors we don't currently measure. Research shows 60% of AI Overview citations come from URLs not ranking in the top 20 organic results. Authority signals matter, and they're tracking more of them.
Unique checks we lack. Their multi-tab interface segments information into SEO, AEO, and GEO categories. Some users prefer this compartmentalization over our single-report approach.
Market presence. They've been operating longer and have developed tooling beyond basic audits.
What we do differently
Scoring that doesn't contradict itself. One score per check. You won't see the same metric showing 0, 10/10, and "Missing" in different report sections.
Actually free free tier. No email required. No $2.49 paywall. Permanent access. Shareable reports.
FAQ-schema pairing analysis. We're the only tool checking whether FAQ content has matching FAQPage schema on the same page. This pairing matters: FAQ format achieves 41% AI citation rates versus 15% for unstructured content, but only when schema markup tells AI systems the content exists.
21 specific AI crawlers. We check GPTBot, ChatGPT-User, OAI-SearchBot, Claude-Web, ClaudeBot, PerplexityBot, GoogleOther, and 14 others individually. AI crawler traffic grew 305% between May 2024 and May 2025. Knowing which specific crawlers are blocked beats a binary "allowed/blocked" answer.
5-layer scoring algorithm. Synergy bonuses when optimizations work together. Severity penalties for critical gaps. Access gate multipliers. This rewards holistic implementation rather than checkbox completion.
Published methodology. We document why each check matters with citations to the Princeton GEO study, Search Engine Land experiments, and Relixir research. Our scoring logic is public.
Implementation-ready output. The $199 premium tier provides copy-paste code blocks. Actual robots.txt rules. Actual llms.txt files. Actual schema markup. Not recommendations. Fixes.
The honest assessment
AI Rank Lab has built a tool with genuine breadth. They check factors we don't. That coverage has value.
Three problems undermine it:
Scoring inconsistencies. The same metric showing 0, 10/10, and "Missing" in one report isn't a feature. Whether bugs or design choices, it creates confusion that makes the data hard to trust.
Pricing bait-and-switch. "Free scan" that costs $2.49 to view damages trust before the relationship starts.
Inflated marketing. "100+ factors" means ~20 checks with a point system. Not uncommon in the industry, but worth understanding before you buy.
The lesson: breadth without consistency creates confusion rather than clarity. We'd rather do seven things well than twenty things unreliably.
What to do next
Start with technical foundation. Run Stobo's free audit. Fix robots.txt configuration, llms.txt implementation, and FAQ-schema pairing first. These barriers block AI citations regardless of content quality. Until they're fixed, nothing else matters.
Then consider broader coverage. Once technical foundation is solid, tools like Otterly.ai, AirOps or AI Rank Lab can identify content signals we don't measure. E-E-A-T, entity recognition, and Knowledge Graph optimization matter for authority building. Just verify their scores manually before acting on them.
If you can only pick one: Start with Stobo. Technical barriers have the highest correlation with AI citations. Research shows 87% of pages cited by AI use proper heading structure. We check that. Blocked crawlers make all other optimizations irrelevant.
Questions about this comparison
"Why point out their contradictions?"
Because users deserve accurate information when choosing tools. We tested our own site with their platform and reported what we found. These are observations, not opinions. The inconsistencies might be bugs. Either way, users experience the confusion.
"Is AI Rank Lab a scam?"
No. They're a legitimate company with real users. The contradictions are concerning but don't make the tool worthless. Their broader coverage includes factors we don't measure. Some users will find value in that breadth despite the scoring issues.
"Should I use both?"
If you want comprehensive coverage, yes. Stobo handles technical foundation with depth. AI Rank Lab provides broader surface coverage. The tools are more complementary than competitive.
"Which is more accurate?"
Depends what you're measuring. Our scoring is consistent: one value per check, no contradictions. They check more things but with less reliability per check. We're accurate about 7 things. They're inconsistent about 20.
The bottom line
Different tools for different needs. They optimize for breadth. We optimize for depth and implementation.
The AEO market is young. Multiple tools can win. We're not enemies. We're different approaches serving different users.
Run the free audit at trystobo.com. See what's blocking your AI citations. Then decide if you need broader coverage from other tools.
Last updated: December 30, 2025
Based on actual AI Rank Lab audit report for trystobo.com