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otter.ai

otter.ai

Moderate AI visibility with 30 of 48 criteria passing. Biggest gap: llms.txt file.

54/100
1 since v1
F
Citation Avg
Answer Readiness
~40% weight
7/10
Content Structure
~25% weight
6/10
Trust & Authority
~15% weight
5/10
Technical Foundation
~10% weight
5/10
AI Discovery
~10% weight
4/10

Verdict

Below-average AEO readiness at 54/100 - multiple areas need attention. Key strengths include Original Data & Expert Analysis, Fact & Data Density, and Canonical URL Strategy. Priority gaps: llms.txt File, RSS/Atom Feed, and Content Licensing & AI Permissions.

#3093of 13363
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How to Improve

Current
54/100
Projected
82/100
39Total Fixes
11Quick Wins
~73hEst. Effort
Quick Wins
Create /llms.txt fileAdd question-based headingsConfigure robots.txt for AI crawlersBuild FAQ sections with schemaImplement semantic HTML5 elementsAdd structured tables and listsAdd ai.txt and content licensingImprove query-answer alignmentAdd visible date signalsMake content creators clearly visibleClarify entity boundaries
Create /llms.txt file
critical|low
Add llms-full.txt with extended content
medium|low
Configure robots.txt for AI crawlers
high|trivial
Fix HTML structure and meta tags
low|low
Create complete sitemap.xml
medium|low
Reduce render-blocking resources
medium|low
Reduce document weight
medium|low
Improve server response efficiency
medium|low
Add internationalization signals
medium|low

Top Opportunities10

Ensure blog content consistently covers your core expertise areas rather than scattering across unrelated topics. AI engines build authority models - a site about "Medicare coverage" that also publishes about humidifiers and groceries dilutes its topical authority.

Expand articles to 1000+ words with structured H2/H3 sections, comparison tables, and expert analysis. Thin content (under 300 words) is rarely cited by AI engines. Deep, well-structured articles demonstrate expertise.

Include "our analysis", "our data", "our testing" phrases backed by original research or proprietary data. 52.2% of AI-cited posts contain owned data signals.

Add question-based headings (H2/H3) throughout your content. Use "What is...", "How does...", "Why should..." patterns that match how users query AI assistants.

Use HTML tables for comparison data and ordered/unordered lists for features, steps, and specifications. Structured data formats are directly extractable by AI engines for answers.

Ensure every question-format heading (H2/H3) is followed by a direct answer paragraph. This pattern is ideal for AI engine snippet extraction.

Place a concise 40-80 word answer block in the first 300 words of each page. Avoid throat-clearing openers like "In this article..." and lead with the answer.

Define the primary entity in the first 500 characters, use consistent terminology (same term 70%+), and add "unlike X" signals to help AI engines distinguish your topics.

Write 20-25 word self-contained answer sentences immediately after each H2 heading. 72.4% of AI-cited posts use this pattern - it gives engines a ready-made snippet to quote.

Show direct use, testing, implementation, or lived experience with concrete observations, examples, screenshots, and lessons learned.

Fix It With AI42

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Site Pages

otter.ai/resourcesF36
Resources | Otter.ai
otter.ai/blogD40
Blog | Otter.ai
otter.ai/sdr-agentF37
Otter.ai - SDR Agent
Changes since v1Last audited 52 days ago
55
54
-1
View full comparison →
Focus Content on Core TopicsIncrease Content DepthAdd Owned Data SignalsRestructure Content as Q&AAdd Structured Tables & ListsImprove Question-Answer AlignmentAdd Answer-First PlacementAdd Entity DisambiguationAdd Answer Capsule PatternsAdd First-Hand Experience Signals

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