Recover the 10-25% of Billable Time Your Team Forgets to Enter

AI billable hours capture reviews calendar, email, document, and project management activity and infers likely billable time as approval-ready suggestions. Each suggestion shows the source activity, the matched engagement, the suggested duration, and the chargeability classification. Timekeepers approve, edit, or reject in seconds rather than reconstructing time from memory.

The most efficient approach is to capture time from activity rather than entering it from memory. Activity-based capture is faster, more accurate, and lower-friction than manual entry. Calendar, email, and document activity already happens; AI infers the time and asks for approval. The 'keeping track' problem becomes a 'reviewing suggestions' problem, which is structurally easier.

How to track billable hours is a question that has gotten more expensive over time, not less. Manual entry at end of week is the dominant pattern at most firms - and it produces a 10-25% time leak as people forget what they worked on. Timer apps (Harvest, Toggl) tighten capture but require professionals to remember to start the timer, which they don't. AI-based time capture closes the gap by reading the activity log - meetings, emails, documents touched, chat threads - and producing draft time entries the professional approves. The result: the firm recovers most of that leakage from work that was already happening, which translates directly to revenue with no additional client effort. The most expensive way to track billable hours is to forget them; AI capture makes forgetting structurally hard.

Adoption is typically high because the friction profile inverts. Manual time entry is high-friction; activity-based capture is low-friction. Most firms see consultant or attorney satisfaction with time tracking improve post-deployment because the daily reconstruction chore largely disappears.

Law firms, accounting firms, consulting firms, and any professional services firm where billable hours drive revenue. The use case is industry-agnostic; the recovered revenue percentage (typically 10-25%) is consistent across industries. Smaller firms (sole practitioners) benefit but the absolute dollars are smaller; the strongest ROI is at firms with 10-500 timekeepers.

On established engagement and timekeeper patterns, accuracy lands at 90%+ on engagement matching and 95%+ on duration estimates. The agent learns from approval and override patterns over the first month of use. Edge cases - context switches, brief touches across multiple matters - surface with surrounding context for timekeeper judgment.

The agent reads activity metadata (calendar attendees, email subject and threading, document collaborator and timestamp) rather than ingesting full content into shared models. What gets analyzed is configurable to your firm's data-handling policy. Most firms find metadata-only analysis is sufficient for billable matching and avoids confidentiality concerns.

Yes - BigTime, Harvest, Mavenlink, Kantata, Replicon, ClickTime, Toggl Track, plus practice management systems with native time tracking (Karbon, Canopy, Practice CS, Clio, MyCase). Approved time writes directly to your system of record.

Implementation is fixed-bid after a scoping call. Most engagements run $20K-$60K depending on firm size and integration complexity. Payback is typically inside 2-3 months from recovered billable revenue alone.