UK Accountancy Firm AI Visibility Checklist

AI models use professional membership numbers to verify firm legitimacy. Without it, they cannot confirm you are a qualified practice.

Plain text is for humans. JSON-LD is machine-readable and directly consumed by AI crawlers including GPTBot and ClaudeBot.

Cross-referencing your website data against the official register is how AI models verify your credentials are current and legitimate.

Any mismatch between your register entry and website reduces AI confidence in your firm identity and suppresses recommendations.

Companies House data is a high-trust source for AI models. Including it as a structured identifier strengthens your entity verification.

AccountingService is the correct Schema.org type for accountancy practices. It signals to AI exactly what category of service you provide.

Generic schema is ignored. Explicit service listings (R&D tax credits, management accounts, self-assessment) allow AI to match you to specific client queries.

AI models use areaServed to match local recommendations. Without it, you may be excluded from location-specific queries even in your own city.

The description field is one of the first things AI models extract when building a firm summary for a recommendation response.

Invalid schema is ignored by crawlers. Validation errors mean your structured data is not being read regardless of what it contains.

ICAEW’s directory is a primary authoritative source for AI models recommending UK chartered accountants.

AccountingWEB is a high-domain-authority UK accountancy resource that AI models cite regularly in recommendation responses.

Google AI Overviews pulls heavily from GBP for local recommendations. An incomplete or unverified profile means you’re invisible in local AI queries.

NAP inconsistency is one of the most common reasons AI models exclude firms from recommendations. A single mismatch reduces confidence across all sources.

TendorAI’s directory is crawled by AI models and combines regulatory verification with schema — creating a single authoritative source for your firm data.

AI models need dedicated, specific content to match your firm to specific client queries. A single generic services page is not sufficient.

AI tools prefer to cite content that directly answers questions in the format they reproduce. Q&A content is the highest-citation content type.

AI models use About page content to build entity summaries. Vague About pages produce vague or no recommendations.

Local AI queries are the fastest-growing category for professional services. Location-specific content directly improves local recommendation rates.

Regulatory references in content reinforce the schema signals and help AI models build a consistent, trustworthy picture of your firm.

You cannot improve what you don’t measure. Manual testing takes two minutes and immediately reveals whether your current setup is working.

Perplexity is currently the most active AI platform for citing specific UK professional services firms by name.

One-off checks miss trends. Regular tracking shows whether changes you make are improving your recommendation rate.

AI visibility is relative. If a competitor improves their setup, your relative recommendation rate falls even if your absolute score stays the same.