How to Add Schema Markup to a Professional Practice Website

Schema markup is structured data that tells machines exactly what your website describes: who the firm is, where it operates, what it does. AI engines parse JSON-LD reliably and use it to confirm identity, authorship, and topic — inputs to whether they cite you. For a professional practice, four schema types do most of the work.

Which schema types does a professional firm need?

Schema type	What it does	Priority
LegalService / AccountingService / MedicalBusiness	Defines the firm as a typed local entity	Required
FAQPage	Marks Q&A content for extraction	Required where FAQs exist
Article + author	Establishes authorship and dates on content	Required on every article
Person	Defines individual practitioners and credentials	High value

Use JSON-LD in a <script type="application/ld+json"> tag — not microdata. It is the format every engine documents and parses best.

Step 1 — Define the firm entity

Place this on the homepage. Use the most specific type that fits: LegalService, AccountingService, MedicalBusiness, or ProfessionalService as the general fallback.

{
  "@context": "https://schema.org",
  "@type": "LegalService",
  "name": "Your Firm Name",
  "url": "https://yourfirm.com",
  "telephone": "+1-512-555-0100",
  "address": {
    "@type": "PostalAddress",
    "streetAddress": "100 Congress Ave",
    "addressLocality": "Austin",
    "addressRegion": "TX",
    "postalCode": "78701",
    "addressCountry": "US"
  },
  "areaServed": "Austin, TX",
  "knowsAbout": ["Estate planning", "Probate", "Trust administration"],
  "priceRange": "$$$"
}

The critical fields for entity resolution are name, address, and knowsAbout. The name and address must match your Google Business Profile, Bing Places, and directory listings character for character — inconsistency is the most common reason engines fail to resolve a firm as one entity.

Step 2 — Mark up practitioners

Engines increasingly answer "who is the best X" with people, not just firms. Each attorney, CPA, or physician bio page should carry:

{
  "@context": "https://schema.org",
  "@type": "Person",
  "name": "Jane Smith",
  "jobTitle": "Managing Partner",
  "worksFor": { "@type": "LegalService", "name": "Your Firm Name" },
  "alumniOf": "University of Texas School of Law",
  "knowsAbout": ["Estate planning", "Asset protection"],
  "sameAs": [
    "https://www.linkedin.com/in/janesmith",
    "https://www.avvo.com/attorneys/janesmith"
  ]
}

The sameAs array matters most: it links the person to their profiles on platforms AI engines already trust, letting the engine merge those reputations into one entity.

Step 3 — Mark up FAQs

FAQPage schema carries outsized weight in citation studies. Two rules from extraction research: the question text in the schema must match the visible heading exactly, and 4–6 questions outperform longer lists — testing shows 20+ questions add no additional citation lift.

{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [{
    "@type": "Question",
    "name": "How much does estate planning cost in Austin?",
    "acceptedAnswer": {
      "@type": "Answer",
      "text": "Most Austin estate plans range from $2,000 to $6,000 depending on complexity. A basic will package sits at the low end; trusts and asset-protection structures at the high end."
    }
  }]
}

Note the answer format: 40–60 words, self-contained, with specific numbers. That is the shape engines extract.

Step 4 — Validate and monitor

Run every page through Google's Rich Results Test and the Schema.org validator. Then confirm the pages are indexed in both Google Search Console and Bing Webmaster Tools — ChatGPT retrieves from Bing's index, and an unindexed page is invisible regardless of its markup.

Schema is necessary but not sufficient: it makes you legible, not recommended. The recommendation comes from the citation and authority work described in how ChatGPT chooses which firms to recommend.