Here is every data point AI looks for when evaluating a house cleaning company, where that data actually lives, and what it can already find.
When an AI system decides which House Cleaning company to recommend, it assembles evidence across every category below. The more complete and verifiable the data, the more confident the recommendation.
The single most differentiating category. Almost no cleaning company has this data published in a structured, machine-readable format. When it is available, AI systems weight it more heavily than any other signal.
AI needs to know what kind of cleaning work you do, not just that you clean. The query "who does move-out cleaning in Austin?" requires a precise match that a general house cleaning 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.
House cleaning is one of the least-licensed service verticals. Most states do not require a trade-specific license to operate a cleaning business. This does not mean licensing is irrelevant — it means the licensing signals that do exist carry outsized weight precisely because most companies only meet the bare minimum.
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.
In a vertical with minimal licensing requirements, industry certifications carry disproportionate weight. They are voluntary signals of professionalism that help AI systems differentiate between companies when regulatory data provides little distinction.
Voluntary memberships and accreditations that serve as corroborating evidence of professionalism. AI systems check these directories when other structured data is limited.
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.
AI cross-references general review platforms with home services marketplaces when evaluating house cleaning companies.
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.