A new name is only linked to an existing person or company when the AI positively confirms it's the same one.

Otherwise it's treated as new and kept separate. This is what lets 'Lin Wei-Chen' — a different ordering of the same name — correctly match the existing 'Wei-Chen L.' record instead of being logged as a different person.

A known ownership percentage can never be silently erased by a later, less specific document.

If a new document doesn't state a specific percentage for a relationship that's already on file, the existing figure is kept rather than cleared. This protects a confirmed stake from being lost just because a later document mentioned the relationship without repeating the number.

Indirect ownership through a chain of companies is calculated independently, not estimated by the AI.

The AI is only asked to report direct relationships a document actually states — for example, 'Company A owns 70% of Company B.' Any indirect stake reachable through a chain of companies is then calculated separately by the system itself, so the same ownership can't be counted twice.

Every confidence score comes with a written reason, and the system leans cautious.

The AI is never allowed to return a bare number — each score must include a short explanation. It's also instructed to weigh the two directions differently: evidence that contradicts a claim should lower confidence sharply, while evidence that supports it should raise confidence more gradually. Missing a real problem is treated as more costly than being overly cautious.

National ID numbers and dates of birth are never sent to the AI model.

When a submitted document contains one, it's compared locally, within our own systems, against the record already on file — the AI is only ever told whether that comparison matched, not the number itself. This keeps the raw identifier from ever reaching the third-party AI provider, while still letting the system use a positive match as supporting evidence when resolving a name variant.