A dev agency replaced hours of manual timesheet review with an AI tool that auto-tags every line item and surfaces per-project cost and effort in a live dashboard.
Developer timesheets are messy. Entries like "worked on auth bug," "client call re: dashboard," and "deploy + hotfix" don't map cleanly to project codes or billing categories. Someone — usually a team lead or ops manager — had to read every line, make a judgment call, and categorize it manually before invoices could go out.
For a 10-person agency billing across 8–12 active projects, that was 4–6 hours per billing cycle just on categorization. Worse, the data existed nowhere useful afterward. There was no easy way to ask "how many hours did we actually spend on Project X last month?" or "which clients are we undercharging relative to effort?"
AI Tagging Engine. Each timesheet line is run through an LLM prompt that classifies it against a configurable list of projects and billing categories. The model returns a tag, a confidence score, and a short reasoning note. High-confidence tags are applied automatically. Low-confidence ones (usually edge cases or ambiguous entries) surface in a lightweight review queue for a human to approve in one click.
Dashboard. Once entries are tagged, the data flows into a simple dashboard: hours per project, cost per project (hours × blended rate), and a running budget burn for each active engagement. The view is updated daily as new timesheets come in. No exports, no spreadsheets, no waiting for the billing cycle — the numbers are always current.
Billing Export. When it's time to invoice, the system generates a clean export per project — all tagged line items, hours, and totals — formatted to drop straight into their invoicing tool. What used to take half a day is now a 5-minute export.
The agency didn't have a time-tracking problem — they had a data extraction problem. The raw hours were being logged. The issue was that converting those logs into structured, queryable data required human judgment at scale. AI is a good fit for exactly this kind of task: free-text classification where the rules are fuzzy but a trained model can make good decisions fast, with a human fallback for the edge cases. The dashboard that comes out the other end turns a billing headache into actual business intelligence.