Onboarding a member business, a vendor, or a new customer usually means collecting a stack of documents: tax certificates, IDs, signature declarations, residence and criminal records, contracts. Someone reads each one, checks a few fields by eye, and hopes nothing was missed. It's slow, inconsistent, and impossible to audit at scale.
Extraction is only half the job
AI can read a document and extract fields, a tax number, an issue date, whether a signature is present. That's helpful, but extraction alone doesn't tell you whether the document is valid. A tax certificate might be extracted perfectly and still be expired. An ID might be read correctly but belong to the wrong person.
Rules turn data into decisions
The reliable pattern pairs AI extraction with explicit verification rules. Each document gets its own rules: this field must be present, this value must equal the applicant's ID from the application context, this date must still be valid within a threshold that depends on the risk profile. The document passes only when its data satisfies the rules.
Group documents into a bundle and you can go further: require certain documents conditionally (online sellers must also submit a domain registration and a distance sales contract), and add cross-document rules that check the name on the ID matches the name on the signature declaration.
Fast, consistent, auditable
The result is a review process that runs in seconds instead of days, applies the same standard to every applicant, and leaves a complete trail of what was checked and why. A human reviewer still signs off where judgment matters, but they start from a verified, structured result instead of a pile of PDFs.