Private AI Stack Boundaries

The existing watch line has moved from questions about on-premise models and an offline meeting tool into task-specific products that keep sensitive data on the device. EastShield offers offline redaction of Chinese and Japanese personal data with a local encrypted restoration map; an independent receipt-scanning app runs a small vision model on a phone and keeps receipt data local by default. Together with earlier records, the line has multiple observed days, three collection sources and at least five publications. These are vendor and builder claims, not independent audits of every downstream data path, so the public stage should not imply certified privacy.

Google released a meeting-note product that transcribes meetings completely offline, turning local processing from a model preference into a packaged workflow boundary. It directly addresses one route by which sensitive conversations leave a private stack, but it does not yet cover evaluation, telemetry, embeddings or downstream integrations. The line remains on watch until whole-workflow egress controls appear across products.

An on-prem operator identifies cloud parsers, hosted evaluators, tracing and embeddings as paths by which data can leave an otherwise local LLM stack; a separate small-business document builder describes the privacy-versus-hardware constraint. The potential category is whole-workflow egress assurance rather than another local model runtime. Both are Reddit reports, so neither the prevalence nor willingness to pay is established. Confirm through network-level audits, repeat enterprise requirements and tools that enforce local processing across ingestion, evaluation and telemetry.