Overall AI readiness score
A combined result across all audit signals.
Average
AI readiness audit: 7/21/2026Public technical data
Overall AI readiness assessment
How ready almeta.cloud is for AI systems
Almeta ML was independently audited by llmsmap. almeta.cloud currently scores 72/100 for AI readiness. The result combines AI-specific files, crawler policy, sitemap discovery, structured homepage data, and—when available—the mobile Google Lighthouse technical profile.
Audit context
llms.txt is accessible and contains 696 tokens. No accessible llms-full.txt was detected, so deeper context still has to be assembled from regular pages. No separate ai.txt policy was detected; it is optional, but can clarify training, retrieval, and attribution preferences.
robots.txt is available. 11 of 11 tracked AI bots are not blocked. Declared sitemaps: 0. The homepage exposes Schema.org types WebSite, Organization, FAQPage; 5 OpenGraph tags were found and markup completeness is 90%.
The mobile Lighthouse profile adds Performance 47/100, Accessibility 96/100, Best Practices 77/100, SEO 100/100, and experimental Agentic Browsing 49/100. These signals have a limited weight: they complement rather than replace llms.txt, robots.txt, and structured-data checks.
Confirmed strengths
- Accessible llms.txt
- Schema.org: WebSite, Organization
- Complete social metadata
Priority improvements
- 1Add llms-full.txt for richer first-party context.
- 2Declare the current sitemap.xml in robots.txt.
- 3Reduce mobile rendering delay and main-thread work.
llms.txt tokens696
llms-full.txt tokens—
ai.txt
sitemap.xml
Google Lighthouse technical profile
A mobile Lighthouse measurement. Google’s experimental Agentic Browsing category is explained separately and does not replace the broader llmsmap AI-readiness score.
47
Performance
96
Accessibility
77
Best Practices
100
Technical SEO
49
Agentic Browsing
What these results mean
Mobile performance is 47/100, with the largest visible content block appearing in 12.4 s and the browser main thread blocked for 510 ms. Layout shift was 0.259. The main thread is where the browser runs JavaScript, calculates layout, and paints the page; long work there delays both user input and browser-agent actions.
Accessibility scored 96/100, Best Practices 77/100, and technical SEO 100/100. The experimental Agentic Browsing category scored 49/100. It measures signals Google currently tests for software agents and is shown separately from the llmsmap AI-readiness score.
1
Free the main thread and accelerate the first view
Split long JavaScript tasks, defer non-critical scripts and styles, and shorten blocking request chains. This helps the primary content appear sooner and makes controls usable earlier.
2
Reduce code that loads without helping the page
Remove unused CSS and JavaScript, load heavy widgets on demand, and limit third-party scripts. Less code means less parsing and background work on the device.
3
Optimise images and their loading order
Serve correctly sized modern formats, prioritise the primary visual, and lazy-load content below the first viewport.
4
Make controls unambiguous
Give buttons and links accessible names, associate labels with fields, and use ordered headings and semantic regions. The same structure helps screen readers and software agents understand actions.
FCP2.0 s
First content
LCP12.4 s
Main content
CLS0.259
Layout stability
TBT510 ms
Blocking time
SI2.8 s
Visual speed
Metric glossary
- FCP · First content
- When the first text or image appeared on screen.
- LCP · Main content
- When the largest visible element in the first viewport rendered.
- CLS · Layout stability
- How much content shifted unexpectedly while loading; lower is better.
- TBT · Blocking time
- How long the browser main thread could not respond quickly to input.
- SI · Visual speed
- How quickly the visible viewport filled with content.
7/21/2026Lighthouse 13.4.0Mobile profile
AI readiness checks
Machine-readable files, crawler policy, discovery, and homepage markup.
llms-full.txt
Full version was not found
ai.txt
ai.txt file was not found
Sitemap in robots.txt
Sitemap is not declared in robots.txt
Schema.org (JSON-LD)
Types: WebSite, Organization, FAQPage
OpenGraph90%
5 OG tags found
AI bot access
Based on robots.txt analysis
GPTBotNot mentioned
OAI-SearchBotNot mentioned
ChatGPT-UserNot mentioned
Google-ExtendedNot mentioned
ClaudeBotNot mentioned
Claude-SearchBotNot mentioned
Claude-UserNot mentioned
BytespiderNot mentioned
CCBotNot mentioned
PerplexityBotNot mentioned
Perplexity-UserNot mentioned
OpenGraph tags
Social preview metadata found on the homepage.
og:typewebsiteog:titleAlmeta ML: Predict Customer Behavior on Your Websiteog:urlhttps://almeta.cloud/og:descriptionOptimize marketing spend with machine learning. Propensity to purchase, product recommendations, best time to contact and other predictive metrics.Schema.org markup
Structured entities and properties exposed on the homepage.
WebSiteOrganizationFAQPage
nameAlmeta MLurlhttps://almeta.cloud/descriptionPredict customer behavior on your website with machine learning. Optimize marketing spend, increase revenue, and improve conversion rates.sameAshttps://x.com/AlmetaCloud, https://linkedin.com/company/almeta-cloud/# Almeta ML > A machine learning service for predicting customer behavior on websites in real-time. Provides built-in ML models for likelihood to purchase, churn prediction, product recommendations, send time optimization, lead scoring. Almeta ML enables businesses to run predictive analytics directly on their websites using event tracking similar to Google Analytics or Facebook Pixel. The platform processes customer action data to predict future behavior through machine learning models, with results delivered in real time to the website, advertising networks, email platforms, CRMs, and personalization engines. ## Features - [Almeta ML](https://almeta.cloud): Product overview, pricing, testimonials, and signup - [Likelihood of an Event](https://docs.almeta.cloud/ml-models/propensity): Calculate likelihood of an event based on historical data and user behavior - [Personalized Product Recommendations](https://docs.almeta.cloud/ml-models/product-recommendations): Generate personalized product recommendations based on user behavior and preferences ## Getting Started - [Introduction](https://docs.almeta.cloud/): Overview of Almeta ML service and prediction examples - [Core Concepts](https://docs.almeta.cloud/concepts): Understanding Events, Models, Destinations, and Catalog data fundamentals - [Getting Started Guide](https://docs.almeta.cloud/getting-started): Step-by-step setup process including account creation, data sources, model selection, and destinations ## Implementation - [Web Tag Installation](https://docs.almeta.cloud/web-tag): JavaScript snippet installation guide with Google Tag Manager integration and consent management - [Web Tags and Models](https://docs.almeta.cloud/web-tag-model-calculations): Running an ML model calculation directly from a web tag - [Events Documentation](https://docs.almeta.cloud/events): Comprehensive event tracking reference with standard and custom event examples ## API - Base URL: `https://api.almeta.cloud/ml/` - [API Authentication](https://docs.almeta.cloud/api/): Bearer token authentication setup and usage - [Events API](https://docs.almeta.cloud/api/events): API usage for programmatic event insertion - [ML Model Calculations API](https://docs.almeta.cloud/api/ml-model-calculations): API usage for programmatic ML model calculation execution - [Web Tags API](https://docs.almeta.cloud/api/web-tags): API usage for programmatic web tag creation and management ## Marketing Integration - [Audiences](https://docs.almeta.cloud/audiences): Creating ML-based audiences for Google Ads, Meta Ads, and other advertising networks ## Optional - [Expert Installation](https://almeta.cloud): Professional setup service, including web tag installation, event tracking configuration, and model implementation
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Added 7/19/2026