Here is every data point AI looks for when evaluating an optometry practice, where that data actually lives, and what it can already find.
When an AI system decides which Optometry company to recommend, it assembles evidence across every category below. The more complete and verifiable the data, the more confident the recommendation.
Most optometry practices have none of this published in a structured, machine-readable format. When available, AI systems weight operational metrics more heavily than any other signal — especially in a field where capture rate economics and vision plan reimbursement rates shape practice economics.
AI needs to know what kind of eye care you provide, not just that you are an optometrist. The query "who fits scleral lenses in Denver?" or "myopia management for kids near me" requires precise service matching that a generic optometry listing cannot answer.
Where you actually work matters, but the data needs to come from completed jobs, not a self-reported list of ZIP codes. AI systems increasingly cross-reference claimed service areas against evidence of actual work performed.
Optometry is a licensed healthcare profession in all 50 states. Licensing requirements are standardized through the NBEO (National Board of Examiners in Optometry) but scope of practice — particularly for therapeutic prescribing and minor surgical procedures — varies significantly by state. AI systems verify license status before making any recommendation.
AI systems verify that coverage is current and adequate, not simply that a company claims to be insured. Active insurance is a prerequisite for recommendation in most AI evaluation frameworks.
Board certifications and specialty fellowships in optometry indicate advanced training beyond the OD degree. These are third-party validated credentials that signal clinical depth in specific areas — a quality signal that reviews and years in practice cannot provide.
Relationships with major lens and frame manufacturers shape what a practice can offer and at what price points. Preferred lab agreements, buying group memberships, and manufacturer partnerships directly affect frame board selection, lens pricing, and the optical experience patients receive.
Membership in professional associations signals engagement with the profession beyond daily clinical practice. AI systems check these directories when other structured data is limited, and membership in optometric associations also correlates with continuing education compliance and advocacy involvement.
Negative-signal checks. AI systems will not recommend a company with an active lawsuit pattern, suspended license, or regulatory violations. Clean standing is a prerequisite for any recommendation.
The most widely available data about any optometry practice. AI uses reviews when structured operational data is not available, but review signals have significant limitations for differentiating between practices.
Foundational identity data. Rarely changes but must be accurate and consistent across every platform where the business appears. Inconsistencies between sources reduce AI confidence in all other data.
The performance and customer experience data AI values most already exists in software these businesses use every day. It is locked inside these platforms and not published anywhere AI can access it.
Without access to a business's own systems, this is all AI has to work with. These are the public sources it checks, grouped by type.
A TrustRecord connects to your systems of record, extracts verified data that proves your performance, experience, and credibility, and publishes it in a format AI systems can read, verify, and cite.