Stop context-switching: AI that passively learns your projects across apps

Executive Summary

An estimated 240 million knowledge workers routinely juggle email, chat, docs, calendars and task trackers, and much of their productivity loss comes from repeated context-switching; organizations spend roughly $300 per user per year on productivity tools yet still lack a reliable way to keep project context unified. The pain is especially acute for distributed and hybrid teams where asynchronous handoffs amplify cognitive overhead and lost time. The product would be an opt-in, privacy-first agent that passively learns project context across apps, maintaining a local index and using hybrid on-device/cloud LLMs to surface summaries, relevant snippets, and next actions without manual tagging or heavy new workflows. Implementation would rely on a browser extension, lightweight desktop agent, and a small set of sanctioned connectors that prefer standard formats to reduce connector maintenance and address integration fatigue; clear provenance, audit logs, and fine-grained permissions would be core differentiators. To stand out against medium-level competition—including incumbents and niche startups—focus on measurable outcomes (minutes saved per day), low-friction adoption, offline/low-latency capabilities, and enterprise-grade privacy guarantees. The market looks attractive now: a $72.0B addressable market driven by hybrid work normalization and the maturation of on-device/hybrid LLMs that make private, low-latency inference feasible. Real challenges remain—earning trust for passive data collection, navigating enterprise procurement, and keeping multi-source inference accurate over time—but with conservative privacy design, demonstrable ROI, and a go-to-market targeting power users and small teams first, this idea has commercially meaningful potential.