Context-aware assistant: capture on-screen work to build a private project memory

Knowledge workers today spend fragmented time across dozens of apps and meetings, losing context and progress between sessions; this problem affects roughly 1.0 billion knowledge workers who could benefit from faster recall of on-screen work and decisions. The result is duplicated effort, longer ramp times for projects, and subjective "memory tax" that is hard to measure but costly across teams. You could build a context-aware assistant that continuously and privately captures on-screen content, audio, and interaction metadata to assemble per-project memories, searchable timelines, and task suggestions, with configurable capture scopes and enterprise-grade access controls. The core product would combine multimodal models for visual and voice understanding with a privacy-first architecture that stores encrypted, per-project indexes locally or in customer-controlled clouds and exposes exportable insights at roughly a $150/year ARPU hypothesis. This market is attractive now because the addressable spend on productivity/assistants is roughly $150.0B (1.0B workers × $150/yr), hybrid work patterns are expanding fractured context, and recent advances in multimodal models make robust screen and audio understanding technically feasible; independent evaluators might score market fit at about 92/100 with revenue potential near 91/100 but competition is medium. To stand out you should emphasize provable privacy guarantees, seamless enterprise integrations (SSO, DLP), and a minimal-friction UX that surfaces project memories without overwhelming users; those are realistic strengths. The primary challenges are engineering complexity (on-device inference, OCR across varied UIs), regulatory/privacy compliance, and customer adoption hurdles, so early enterprise pilots and clear SLA/incident models will be essential to validate product-market fit.