Flyers, follow-ups, handover comms
on every frontier model.
Buyer identities masked before the prompt
leaves your workspace.

"My advisors draft buyer follow-ups in ChatGPT with real passport and contact details. Does every paste leave a trace we can't explain?"

Yes — and that's the problem this removes. Buyer names, phone numbers, emails and ID details — English and Arabic — are replaced with inert tokens before the call; the model answers the anonymized text and your private copy is restored in your workspace. It fails closed: if masking can't run, the request stops. Detection is probabilistic (like every vendor's) — which is why every query also lands in an audit record.

"Can we draft off-plan marketing copy without our buyer and client lists leaking to a model provider?"

Yes. The model writes the flyer, the campaign brief, the launch recap — from anonymized text. Unit registers, buyer shortlists, broker lists and contact sheets never leave your workspace in readable form. The copy comes back, the real names are restored only in your workspace, and every step of that round trip is in the audit record.

"We run several projects. Can Project A's buyers never appear in Project B's work?"

Yes. Each organisation gets its own isolated workspace — separate prompt history, separate audit trail. When a project partner (or their auditor) asks for a record of how their data was used, you export only that workspace's chain. No cross-contamination, no commingled logs.

"Our compliance team will ask which buyer data reached an AI. What do they get?"

The hash-chained audit trail: who queried what, when, through which model, with every masked element recorded. Each entry locks the previous one — tamper-evident by construction. RERA, DLD and UAE data-protection duties stay your firm's obligations — we don't certify you; we give you the record you answer them with.

"Our collateral is bilingual. Does the masking handle Arabic brochures and English buyer lists in the same message?"

Yes — shipped, not on a roadmap. PII detection runs on English and Arabic text, so a mixed-language prompt (an Arabic flyer draft with an English buyer shortlist pasted in) is masked in both scripts before any model call, one governed workspace, no switching tools mid-sentence.