Executive Summary

Knowledge workers and their managers currently lack accurate, granular visibility into where attention actually goes during the workday: browser-based time trackers and app-level analytics commonly log a tab as “active” whenever it is in the foreground or when the browser window is focused, causing substantial misattribution of idle or background attention for individuals, freelancers, and teams in hybrid/remote settings. This problem affects an estimated 150 million global knowledge workers and creates pain for billing accuracy, productivity coaching, and engineering/PM teams trying to understand true focused work versus passive reference time. You could build a privacy-first per-tab active-use analytics product composed of a lightweight browser extension and optional desktop companion that combines interaction heuristics (mouse/keyboard, visibility) with on-device ML classification to infer attention vs idle at sub-second resolution, aggregating results, anonymizing content, and exporting summaries to payroll, PM, and calendar tools. The offering would prioritize edge inference to keep raw activity on-device, provide configurable focus-session markers, and surface per-tab attention metrics (not just open time) so users and managers can distinguish reading, active editing, and passive background consumption. The timing is favorable: the addressable productivity/time-tracking market is roughly $18.0B (150M users × $120/year), remote and hybrid work have increased demand for objective visibility, and the attention-economy narrative is driving willingness to pay for tools that measurably improve uninterrupted focus. On-device ML and browser APIs now make it technically feasible to deliver higher-fidelity, low-latency signals without heavy server infrastructure, which reduces cost and privacy concerns that previously hampered adoption. To stand out you will need to deliver measurably better accuracy (fewer false positives for “active” time), clear privacy guarantees, and seamless integrations with existing tools like Toggl, Clockify, and Slack; positioning should emphasize auditability and user control rather than surveillance. Challenges include user trust and enterprise procurement, the risk of behavioral circumvention, and medium competition from established players, so focus on a clear niche (freelancers, focused engineering teams, or compliance-conscious enterprises), transparent validation metrics, and an easy path to pay (e.g., $5–15/month per user) to prove product-market fit.

Market Opportunity

Track real tab attention time — per-tab active-use analytics targets a $18.0B = 150M global knowledge workers x $120/yr average spend on productivity/time-tracking tools total addressable market with medium saturation and a year-over-year growth rate of 10-15% -- steady growth as remote/hybrid work and productivity tooling expand.

Key trends driving demand: Hybrid/Remote Work -- more distributed knowledge work increases demand for visibility into how time is spent; Attention Economy -- individuals and companies want tools to measure and optimize uninterrupted focus time; Edge/On-device ML -- enables private, low-latency inference to classify active vs idle behavior without heavy server infrastructure; Privacy-first Analytics -- rising user sensitivity pushes solutions that offer opt-in, anonymized benchmarking rather than invasive monitoring.

Key competitors include RescueTime, Toggl Track, Clockify, Hubstaff / Workpuls (employee monitoring adjacent), Browser extensions & workarounds (StayFocusd, Tab Wrangler, manual tracking).

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Track real tab attention time — per-tab active-use analytics

Executive Summary

Market Validation

Market Opportunity

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