Readiness score: 20.0%

• ✗ llms.txt present
  ?

  What: a /llms.txt that describes your service for AI crawlers. Why: it helps language models understand what you do and cite you correctly (like robots.txt, but for AI).
  
  Add an llms.txt that describes your service for AI crawlers.
  llms.txt (place at {base_url}/llms.txt) Copy

  # Finobyte
  > One-line summary of what Finobyte does and who it is for.
  
  https://finobyte.solutions
  
  ## Key pages
  - [Home](https://finobyte.solutions/): what Finobyte offers, in brief
  - [About](https://finobyte.solutions/about): who Finobyte is
  - [Contact](https://finobyte.solutions/contact): how to get in touch
  
  ## Notes
  Written for AI assistants: plain facts about Finobyte, kept current.

• ✓ robots.txt present
  ?

  What: a /robots.txt that controls what crawlers may read. Why: without it, important pages can be missed or the wrong pages indexed.
• ✗ sitemap.xml present
  ?

  What: a /sitemap.xml listing your pages. Why: crawlers and AI find your whole structure so nothing important is missed.
  
  Publish a sitemap.xml so your content is found.
• ✗ Structured data (JSON-LD)
  ?

  What: JSON-LD describing your company and products in a machine-readable way. Why: AI uses it to understand what you are and describe you correctly.
  
  Add schema.org JSON-LD (Organization/Product/FAQ).
  Organization schema (JSON-LD) Copy

  <script type="application/ld+json">
  {
    "@context": "https://schema.org",
    "@type": "Organization",
    "name": "Finobyte",
    "url": "https://finobyte.solutions",
    "logo": "https://finobyte.solutions/logo.png",
    "description": "One or two plain sentences describing what Finobyte does and who it is for.",
    "sameAs": [
      "https://www.linkedin.com/company/your-company"
    ]
  }
  </script>

• ✗ Canonical URL
  ?

  What: a canonical URL points to the 'real' address when the same content lives on several URLs. Why: it stops your visibility being split across duplicates.
  
  Set a canonical URL on each page.
• ✗ Open Graph tags
  ?

  What: Open Graph tags (title/image/description) control how the page looks when shared. Why: they give AI and social media a clear, correct summary.
  
  Add OG tags for sharing and previews.
• ✗ Meta description
  ?

  What: the page's short summary. Why: search engines and AI use it to describe you - a good description means you are described the way you want.
  
  Write a clear meta description on each page.
• ✗ FAQ / questions and answers
  ?

  What: an FAQ with clear questions and answers. Why: it matches how people ask AI - models often lift answers straight from well-structured FAQs.
  
  Add an FAQ that answers buyer questions (ideally with FAQPage schema).
  FAQ schema (FAQPage JSON-LD) Copy

  <script type="application/ld+json">
  {
    "@context": "https://schema.org",
    "@type": "FAQPage",
    "mainEntity": [
      {
        "@type": "Question",
        "name": "What does Finobyte do?",
        "acceptedAnswer": {
          "@type": "Answer",
          "text": "Answer in one or two plain sentences a model can quote directly."
        }
      },
      {
        "@type": "Question",
        "name": "Who is Finobyte for?",
        "acceptedAnswer": {
          "@type": "Answer",
          "text": "Describe your ideal customer in plain language."
        }
      }
    ]
  }
  </script>

• ✓ Page title
  ?

  What: the page title. Why: it is the first thing AI and search engines read - a clear title with what you do helps you get named in the right context.
• ✓ Clear main heading (H1) in the page source
  ?

  What: a main heading (H1) present in the page source. Why: AI crawlers often do not run JavaScript - a static, clear heading means they actually read what you do instead of seeing an empty page.
• ✗ Article schema (JSON-LD)
  ?

  What: JSON-LD of type Article/BlogPosting with author, publisher and dates. Why: it helps AI understand the page is editorial content, who stands behind it and how fresh it is - signals that weigh in when models choose what to cite.
  
  Mark up articles and guides with Article schema (author, publisher, datePublished).
  Article schema (JSON-LD) Copy

  <script type="application/ld+json">
  {
    "@context": "https://schema.org",
    "@type": "Article",
    "headline": "Your article title",
    "author": {
      "@type": "Organization",
      "name": "Finobyte"
    },
    "publisher": {
      "@type": "Organization",
      "name": "Finobyte",
      "logo": {
        "@type": "ImageObject",
        "url": "https://finobyte.solutions/logo.png"
      }
    },
    "datePublished": "YYYY-MM-DD",
    "dateModified": "YYYY-MM-DD",
    "mainEntityOfPage": "https://finobyte.solutions/your-article",
    "description": "One or two plain sentences summarising the article so a model can quote it."
  }
  </script>

• ✗ HowTo schema (JSON-LD)
  ?

  What: JSON-LD of type HowTo for step-by-step content. Why: it makes instructions machine-readable so AI can reproduce your steps correctly instead of guessing.
  
  Add HowTo schema to guides and step-by-step pages.
• ✗ Author (byline/markup)
  ?

  What: a clear author via markup (JSON-LD author, rel=author or meta author). Why: who stands behind the content is a credibility signal (E-E-A-T) that AI weighs when deciding whether to trust and cite you.
  
  State the author with markup (author in JSON-LD, rel=author or meta name=author).
• ✗ Published/updated dates
  ?

  What: datePublished/dateModified in markup or <time> elements. Why: freshness matters - AI often prefers updated content and can show when something was last changed.
  
  Expose published and updated dates in markup (datePublished/dateModified or <time datetime>).
• ✗ Organization entity (sameAs)
  ?

  What: Organization schema with sameAs links to your official profiles (e.g. LinkedIn, Wikidata). Why: it ties your company to a clear entity so AI does not confuse you with someone else and describes you correctly.
  
  Add Organization schema with sameAs links to your official profiles.

The AI models this report covers. New models are added over time and shown here.

• x-ai/grok-4.3
  first measured: 2026-06-24
• deepseek/deepseek-v4-pro
  first measured: 2026-06-24
• z-ai/glm-5.1
  first measured: 2026-06-24
• google/gemini-2.5-flash
  first measured: 2026-06-24
• openai/gpt-5.5
  first measured: 2026-06-24
• anthropic/claude-sonnet-4.6
  first measured: 2026-06-24