Case Studies

The Problem

A family-owned HVAC and plumbing company with 25 technicians was losing time on administrative work. Each technician spent 30–45 minutes per job writing up quotes and invoices, often from their truck using a clunky mobile app. Quotes were inconsistent, frequently missing line items, and customers complained about slow turnaround.

• Technicians spent up to 3 hours daily on paperwork
• Quote accuracy varied widely between team members
• Average quote delivery: 24–48 hours after site visit
• Frequent customer callbacks requesting missing details

Our Approach

We implemented an AI-assisted quote generation workflow that works with their existing field service software. Technicians now speak or type brief notes, and AI drafts complete, professional quotes following company templates.

1. 1Analyzed 200+ historical quotes to understand patterns and common errors
2. 2Built prompt templates for different service types (HVAC repair, plumbing install, etc.)
3. 3Integrated with their field service app via Zapier
4. 4Trained technicians in 2-hour virtual sessions
5. 5Created a review checklist for quality control

Tools Used

The Problem

A 40-person accounting firm spent the first week of every month cleaning client data exports and compiling reports. Staff manually reformatted spreadsheets from various client systems, checked for errors, and generated standardized reports. The process was tedious, error-prone, and stole time from higher-value advisory work.

• 3 staff members dedicated to data cleanup each month-end
• Average 6 hours per client for data processing
• Regular errors requiring rework (estimated 15% error rate)
• Staff dreaded "spreadsheet week"

Our Approach

We built a semi-automated pipeline combining AI for data cleaning with simple scripts for report generation. The AI identifies formatting issues, missing data, and outliers, then suggests corrections. Staff review and approve rather than doing everything manually.

1. 1Documented all data sources and current cleaning procedures
2. 2Created AI prompts for common data issues (duplicates, format mismatches, outliers)
3. 3Built Python scripts to automate report generation from cleaned data
4. 4Set up a review dashboard for staff to approve AI suggestions
5. 5Trained 5 key staff members on the new workflow

Tools Used

The Problem

A growing e-commerce company selling specialty outdoor gear saw support tickets double after a successful product launch. Their 4-person support team couldn't keep up—response times stretched to 48+ hours, customer satisfaction dropped, and burnout was setting in. Hiring wasn't an option due to budget constraints.

• Average response time: 48+ hours (target: 24 hours)
• Support team working overtime, risking burnout
• Inconsistent response quality across team members
• 30% of tickets were simple FAQs answered repeatedly

Our Approach

We implemented AI-powered triage and response drafting. The AI categorizes incoming tickets, drafts responses for common issues, and flags complex cases for human attention. Staff review and personalize drafts before sending.

1. 1Analyzed 6 months of support tickets to identify patterns
2. 2Built triage rules to categorize tickets (FAQ, order issue, technical, escalation)
3. 3Created response templates and prompt library for each category
4. 4Integrated with their helpdesk (Zendesk) via API
5. 5Trained team on reviewing and personalizing AI drafts
6. 6Set up quality monitoring for first 30 days

Tools Used