Executive Summary

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.

Market Opportunity

Context-aware assistant: capture on-screen work to build a private project memory targets a $150.0B = 1.0B knowledge workers x $150/yr ARPU (global productivity/assistants spend) total addressable market with medium saturation and a year-over-year growth rate of 25-35% (productivity AI & knowledge-management segment).

Key trends driving demand: multimodal-models -- enable processing of screen content and audio for richer context; hybrid-work -- increases fractured context across apps, raising demand for unified memory; privacy-first architecture -- enterprises prefer solutions that keep sensitive data private; automation of knowledge work -- increased appetite for assistants that reduce context-switching.

Key competitors include Rewind (rewind.ai), Heyday (heyday.ai), Mem (mem.ai), Fireflies.ai (and other meeting-transcription tools like Otter.ai), Microsoft 365 Copilot / Microsoft Viva.

Sign in to access

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

Executive Summary

Market Validation

Market Opportunity

More in Productivity

Fix fragmented AI workflows with an integrated AI assistant and automated templates

Voice-first writing: get words out fast with AI transcription + editor

Stop tab chaos — autonomous AI agents that orchestrate and automate workflows

Stop starting and quitting projects — automated idea validation + accountability

Automate hours-long manual tasks with LLM-driven workflow automation

Collaboration friction for remote teams — AI-coached workflows & unified async