Saving 14 Hours a Week with AI Email Triage and Response Drafting

800 Emails a Day, Two People to Read Them

A Tampa commercial property management firm handled maintenance requests, vendor coordination, lease inquiries, and tenant complaints through a single shared inbox. On an average day, 800 emails arrived. Two administrative staff members spent their mornings reading and routing messages, their afternoons drafting responses, and their evenings catching up on what they missed.

The real cost wasn't labor. It was delay. A broken HVAC report from a tenant sat in the inbox for 9 hours because it landed between two lease renewal threads. The tenant called their lawyer. The property manager found out when the attorney letter arrived. That single missed email cost $4,200 in legal fees.

The firm had tried inbox rules and filters. They helped with obvious categories (spam, vendor receipts) but couldn't distinguish between a routine maintenance request and an emergency that needed same-day attention.

Teaching AI to Read Like an Office Manager

We built an email triage system with three components: a classifier, a priority scorer, and a draft generator.

The classifier sorted incoming emails into seven categories: emergency maintenance, routine maintenance, lease inquiry, vendor communication, tenant complaint, financial/billing, and general correspondence. It read the subject line, body text, and sender history to make the call. Accuracy after the first week of training on their historical data: 89%. After four weeks of corrections: 96%.

The priority scorer assigned urgency levels. An email mentioning "water leak," "fire alarm," or "no heat" automatically flagged as urgent and sent a text notification to the on-call manager. Lease expiration inquiries within 60 days got medium priority. Everything else queued normally.

The draft generator created response templates based on the category. For routine maintenance, it pulled the tenant's unit number, confirmed the request, and scheduled a work order. Staff reviewed and sent with one click. Average edit time: 45 seconds. Previous draft time: 8 minutes.

Results After 90 Days

The two admin staff didn't lose their jobs. They shifted 14 hours per week from email sorting and drafting to tenant relationship work, move-in coordination, and vendor negotiation. The office manager said the biggest change was that nobody dreaded Monday mornings anymore.

What Made This Work

Three decisions during setup determined the outcome.

First, we trained the classifier on six months of their actual email history (roughly 120,000 messages) rather than using generic categories. Their business had specific patterns that off-the-shelf tools would have missed. "Unit 4B" in a subject line meant a problem tenant with a history of frivolous complaints. The system learned to route those to a specific manager who had context.

Second, we built a correction loop. When staff disagreed with the AI's classification, they changed it with one click. Those corrections fed back into the model weekly. Accuracy improved from 89% to 96% in the first month purely from staff corrections.

Third, we kept humans in the loop for drafts. The AI suggested responses. Humans approved them. This prevented the system from sending a cheerful "we'll get to it next week" response to a burst pipe at 2 AM.

Cost and Timeline

Setup took five weeks. Week one: email history export and category mapping. Weeks two and three: classifier training and testing. Week four: draft template creation and integration with their email platform. Week five: staff training and parallel run (AI and humans processing the same emails to compare results).

Build cost: $12,000. Monthly operating cost: $340 (API usage for classification and draft generation). Annual savings: $24,000 in labor reallocation plus an estimated $8,000-$12,000 in avoided escalations from faster emergency response. Breakeven: 5 months.