Context-aware AI assistant that reads your screen to build private work memory

Executive Summary

Many knowledge workers—estimated 400 million globally—lose hours each week to context switching, re-finding information and re-explaining work across email, docs, chat and web apps; this fragmentation is most acute for hybrid teams and people who juggle more than a dozen tabbed contexts per day. The core problem is not lack of AI capability but lack of persistent, private contextual memory that can surface the right facts and drafts inline when they matter. You could build a context-aware assistant that, with explicit user consent, reads screen content to create encrypted, per-user embeddings and a searchable private work memory, delivering inline summaries, proactive reminders and draft generation across apps via browser extensions, OS accessibility hooks and enterprise SDKs. The architecture would favor privacy-first choices—on-device processing where feasible, client-side encryption of memories, and fine-grained admin controls—while exposing audit logs and usage telemetry that prove ROI to buyers. The timing is attractive: cheaper, higher-quality LLMs and growing vector-search tooling make accurate, low-latency contextual assistants feasible, and hybrid/remote work trends increase demand for cross-app continuity; I estimate a serviceable market around $300 billion (400M knowledge workers × ~$750 annual value per worker), and I’d rate market opportunity and revenue potential as strong (Market Score 92/100; Revenue Potential 88/100). To stand out you must deliver demonstrable accuracy, enterprise-grade privacy and easy integrations, but expect steep competition, platform permission constraints and a high trust bar—winning pilots with measurable time-savings will be essential to scale.