Here is every data point AI looks for when evaluating a mental health or therapy practice, where that data actually lives, and what it can already find. Mental health has unique dynamics: insurance credentialing bottlenecks, wide variation in license types, the rapid expansion of telehealth, and an industry-wide waitlist problem that makes availability data unusually valuable.
When an AI system decides which Mental Health company to recommend, it assembles evidence across every category below. The more complete and verifiable the data, the more confident the recommendation.
Almost no therapy practice publishes structured operational data. When it is available, AI systems weight it more heavily than any other signal — particularly in mental health, where the supply-demand imbalance makes capacity and availability the most sought-after information.
Mental health is not a single service. The query "therapist near me" is almost useless — what matters is modality, population specialty, and presenting concern. AI systems need structured data about what kind of therapy a practice actually provides to match the right clinician to the right patient.
Where a practice actually serves patients matters — but in mental health, telehealth has redefined geography. A practice may see patients across an entire state via telehealth while maintaining a physical office in one city. AI systems need to distinguish between in-person service areas and telehealth coverage states.
Mental health licensing is state-specific and varies by discipline. There are multiple distinct license types, each with different scopes of practice, supervision requirements, and prescriptive authority. AI systems must match the correct license type to the service being sought — a licensed social worker, a psychologist, and a psychiatrist are not interchangeable.
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.
Beyond the base license, specialty certifications signal advanced competence in specific modalities or populations. In mental health, these certifications often determine whether a therapist can effectively treat a specific presenting concern — the difference between a generalist and a specialist.
Voluntary memberships that signal ongoing professional engagement. AI systems use association directories as a cross-reference when structured practice data is limited. In mental health, the major associations also maintain therapist finder directories that are themselves data sources.
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 therapy practice. AI uses reviews when structured operational data is not available, but review signals have significant limitations for differentiating between clinicians — particularly in mental health, where therapeutic fit is highly personal.
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.