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telm.ai
SUMMER 2021

telm.ai

Moderate AI visibility with 36 of 48 criteria passing. Biggest gap: llms.txt file.

SaaS & ProductivityAnalytics
63/100
F
Citation Avg
Answer Readiness
~40% weight
8/10
Content Structure
~25% weight
5/10
Trust & Authority
~15% weight
7/10
Technical Foundation
~10% weight
6/10
AI Discovery
~10% weight
7/10

Verdict

Moderate AEO readiness at 63/100 with significant gaps to address. Key strengths include Internal Linking Structure, Sitemap Completeness, and RSS/Atom Feed. Priority gaps: llms.txt File, Comprehensive FAQ Section, and Speakable Schema.

How to Improve

Current
63/100
Projected
83/100
35Total Fixes
13Quick Wins
~62hEst. Effort
Quick Wins
Create /llms.txt fileConfigure robots.txt for AI crawlersAdd ai.txt and content licensingImprove query-answer alignmentAdd visible date signalsIncrease content depth and structureAdd answer-first content placementPackage evidence for AI enginesClarify entity boundariesReduce extraction frictionImprove server response efficiencyReduce render-blocking resourcesReduce document weight
Create /llms.txt file
critical|low
Add llms-full.txt with extended content
medium|low
Create complete sitemap.xml
critical|medium
Configure robots.txt for AI crawlers
high|trivial
Reduce render-blocking resources
high|low
Reduce document weight
high|low
Add internationalization signals
critical|medium
Improve server response efficiency
high|low
Fix HTML structure and meta tags
low|low

Top Opportunities10

Create a dedicated FAQ page with FAQPage schema markup. Cover common questions about your products, services, and industry to become a direct answer source for AI engines.

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.

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.

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.

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.

Write concise, standalone answer paragraphs (2-3 sentences) immediately after question headings. These "snippet-ready" paragraphs are ideal for AI engine citations.

Add inline citations to external sources, "According to [Source]..." attribution phrases, and a Sources section at the end of key articles.

Fix It With AI37

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Site Pages

www.telm.ai/data-wikiF39
Data Terms Explained | Telmai Data Wiki
www.telm.ai/why-telmaiF39
Why should you choose Telmai for your data stack
www.telm.ai/products/anomaly-detectionF32
Data Anomaly Detection with Telmai
www.telm.ai/industry/financial-servicesD49
Turn data quality challenges into lucrative dividends with Telmai
www.telm.ai/products/data-qualityD41
Telmai | Automated AI Data Quality Platform

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