Executive Summary

Knowledge workers today suffer from coarse, app-level time metrics and manual tagging, which makes billing, task tracking, and productivity analysis inaccurate and time-consuming for an estimated 100M knowledge-worker seats. This pain shows up as lost billable hours, poor project estimates, and frustration with tools that can't map activity to semantic tasks. You could build a background agent that analyzes on-screen content with vision-language models to produce an automatic, timestamped activity timeline with task labels, summaries, and exportable billable estimates, while running inference locally to preserve privacy. The product would surface an editable timeline UI and integrations with time-tracking, billing, and project-management tools so users can correct and certify entries. The market is attractive now: a ~$5.0B addressable market (100M seats × $50 ARPU) with a market score of 82/100 and revenue potential of 80/100, driven by growing demand for semantic time reporting and rising adoption of local, privacy-first AI. Improvements in vision-language models and falling on-device compute costs make automatic, accurate screen understanding far more feasible than two years ago. You can differentiate by combining on-device inference, enterprise-grade privacy guarantees, and high-precision semantic labeling tied directly to workflows and billing, which addresses the core trust and value concerns customers have. The main challenges are obtaining reliable cross-OS screen access, proving accuracy and privacy to enterprise buyers, and navigating medium competition—but if you can deliver demonstrable precision and clear ROI, this idea has real legs.

Market Opportunity

Build an accurate activity timeline by analyzing screen content targets a $5.0B = 100M knowledge-worker seats × $50 ARPU annually for semantic time & productivity tooling total addressable market with medium saturation and a year-over-year growth rate of 8% YoY — estimated growth for productivity and time-tracking SaaS driven by hybrid work and automation adoption (industry estimates for SaaS productivity tools 2022-2025)..

Key trends driving demand: Local and privacy-first AI inference adoption is increasing — this creates demand for tools that can run models on-device and promise data confidentiality.; Knowledge workers are frustrated with coarse app-level metrics — this drives interest in semantic, context-aware time reporting that maps directly to tasks and billing.; Improved vision-language models enable automatic summarization and labeling of screen content — this reduces the need for manual tagging and timers.; Rise of hybrid/remote work increases emphasis on asynchronous visibility and accountability — teams want automated timelines for retrospectives and billing..

Key competitors include Timing, RescueTime, Toggl / Clockify, WakaTime.

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Build an accurate activity timeline by analyzing screen content

Executive Summary

Market Validation

Market Opportunity

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