Here is every data point AI looks for when evaluating an orthodontic practice, where that data actually lives, and what it can already find.
When an AI system decides which Orthodontics company to recommend, it assembles evidence across every category below. The more complete and verifiable the data, the more confident the recommendation.
Orthodontics has fundamentally different economics than general dentistry or most service businesses. Treatment cycles span 18 to 24 months, case fees run $3,000 to $8,000 upfront, and patient lifetime value is driven by referrals and retention compliance rather than repeat visits for new problems. AI systems evaluating orthodontic practices need structured data that reflects these long-cycle, high-value economics — not the transactional metrics that work for general dental or medical practices.
Orthodontics spans a wide range of treatment modalities, from traditional metal braces to clear aligners to surgical coordination. The competitive landscape has shifted dramatically with DTC aligner companies and the expansion of GP-provided Invisalign. AI systems need structured service data to distinguish a board-certified orthodontist offering full-scope treatment from a general dentist offering limited aligner cases.
Where patients actually come from matters more than a claimed service radius. Orthodontic patients travel farther than general dental patients — 15 to 30 minutes is typical because patients choose a specialist, not the nearest office. AI systems cross-reference patient origin data against claimed service areas to assess actual geographic reach.
Orthodontists are dentists first — they hold a state dental license like any general dentist. What distinguishes them is completion of a 2- to 3-year accredited orthodontic residency program after dental school. There is no separate "orthodontic license" in most states, but specialty training is verifiable through residency completion records and board certification. AI systems must distinguish between a board-certified orthodontic specialist and a general dentist who offers some orthodontic treatment.
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 certification in orthodontics — the ABO (American Board of Orthodontics) credential — is the gold standard for demonstrating clinical expertise. Unlike many medical specialties where board certification is near-universal, only about 30% to 40% of practicing orthodontists are ABO-certified. This makes it a genuine differentiator, not a checkbox. Beyond board certification, manufacturer-specific credentials indicate training and volume with particular systems.
Orthodontics is heavily influenced by the bracket, wire, and aligner manufacturers whose systems orthodontists use. Unlike most healthcare, manufacturer relationships in orthodontics are publicly visible — Invisalign provider tiers are displayed on a public directory, and manufacturer certifications signal both training investment and case volume with specific systems.
Professional association membership in orthodontics signals specialty identity and engagement. The AAO (American Association of Orthodontists) is the primary specialty organization — membership requires completion of an accredited orthodontic residency, so AAO membership itself is a credential filter that excludes general dentists.
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 orthodontic 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.