The question we're answering

When someone asks ChatGPT, Perplexity, Gemini, or any AI assistant to recommend a business like yours, can it? Does it have enough structured, verifiable information to confidently include you in the answer?

The AO score measures how close your business is to being consistently recommendable by AI agents. A higher score means more data, better structured, with fewer gaps for an agent to stumble on.

Six dimensions, weighted by impact

Each dimension measures a distinct aspect of AI readability. The weights reflect how much each dimension affects whether an AI agent can actually recommend you. These weights are based on how current AI systems (as of early 2026) retrieve, evaluate, and present local service businesses.

1. Data Readability 25% of score

What it measures: Whether AI agents can read your business information directly, or whether they're guessing from your page text.

This is the single most impactful dimension. When your business data is properly structured, AI agents get clean, unambiguous facts: what you do, where you are, what you charge, what credentials you hold. Without it, they're scraping text and hoping for the best. This is the foundation of algorithmic trust.

2. Service Descriptions 20% of score

What it measures: Whether AI agents can extract a clear, specific list of what you actually do.

A vague "we offer a range of services" is invisible to an agent filtering by specific service type. This dimension checks whether your services are individually named, described, and structured so an agent can match them to what someone is looking for.

3. Pricing Transparency 20% of score

What it measures: Whether an AI agent can answer the question "how much does this cost?" without guessing.

AI agents increasingly filter by budget. If someone asks for "a plumber under 100 for a callout", businesses without any pricing signal are excluded entirely. You don't need to list every price. A starting rate, a price range, or structured Offer data is enough to stay in the conversation.

4. Trust Signals 15% of score

What it measures: Whether there's enough verifiable evidence for an AI agent to recommend you with confidence.

AI agents cross-reference multiple sources. They look for consistency (does the name, address, and phone match across your website and Google?), evidence of quality (reviews, accreditations), and whether your site actively allows AI crawlers to index it.

5. Availability Readiness 10% of score

What it measures: Whether an AI agent can determine where you operate, when you're available, and how to book.

An agent that knows you exist but can't tell if you serve a given area, what your hours are, or how to book is unlikely to recommend you over a competitor with clearer data. This dimension checks the practical "can I actually use this business?" signals.

6. AI Visibility 10% of score

What it measures: What AI systems currently know about your business when asked directly.

We query major AI models with natural-language questions about your business and compare their responses to your actual details. This is the reality check: regardless of what data exists on your site, does the AI actually know who you are?

How the score works

Each dimension is scored 0-100 individually, then multiplied by its weight. The six weighted scores are summed to give your overall AO score out of 100.

Your score places you in one of five bands:

Band Score What it means
Invisible 0 - 25 AI agents cannot confidently evaluate or recommend your business
Fragmented 26 - 50 Some signals present but major gaps remain
Visible 51 - 75 AI agents have some data but can't confidently recommend your services
Ready 76 - 90 Well-positioned for confident AI agent recommendation
Optimised 91 - 100 Fully agent-ready across all dimensions

Why these dimensions?

These aren't arbitrary categories. They map to the actual process an AI agent follows when deciding whether to recommend a business:

1
Can I read this business's data? Data Readability dimension. Without properly structured information, the agent is guessing from scraped text.
2
Does this business offer what the person needs? Service Descriptions dimension. The agent needs to match specific services to specific queries.
3
Can I give a price estimate? Pricing Transparency dimension. Budget filtering is increasingly common in AI queries.
4
Can I trust this recommendation? Trust Signals dimension. AI agents need verifiable, consistent evidence before recommending.
5
Can this person actually use this business? Availability Readiness dimension. Location, hours, and booking capability.
6
Do I already know this business? AI Visibility dimension. The reality check against what AI systems currently know.

What we don't score

The AO Audit is specifically about AI agent readability. It does not measure:

A beautiful website with no AI-readable data will score poorly. An ugly website with excellent algorithmic trust signals will score well. That's by design: we're measuring what machines can read, not what humans see.

Verifiable outputs

When we implement AO recommendations, every change produces verifiable output. The data we add to your website can be tested using Google's Rich Results Test and the Schema Markup Validator. You can paste your URL into either tool and see exactly what AI-readable information exists, whether it's valid, and what it contains.

This isn't a trust-me deliverable. It's testable, standards-based, and visible to anyone who checks.

Industry profiles

Not all businesses need the same data. A therapist's ideal profile looks different from a plumber's. The audit uses industry-specific profiles that adjust expectations: which data points matter most, which accreditations are relevant, which services a business of your type would typically offer.

Current profiles: therapist/wellness, interpreter/language services, and a generic profile that works for most local service businesses. We're building more as we audit more industries.

What's coming next

The current audit focuses on your website and on-site signals. But AI agents don't just read your site — they cross-reference multiple sources. We're expanding the methodology to include off-site signals that increasingly affect whether you get recommended:

These signals will be integrated into the audit as we validate their impact through real-world testing. We'd rather add dimensions that are proven to matter than pad the score with guesses.

Limitations and honesty

This methodology is version 1. Here's what you should know:

We'd rather be transparent about the edges than pretend they don't exist.

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