Servo Detail offers an AI‑powered business management platform tailored for auto detailers, enabling them to book jobs, invoice clients, and manage their service catalog entirely by voice. The hands‑free interface eliminates app‑switching, allowing detailers to create invoices instantly after a job, which speeds up payment collection and reduces missed billing. It’s designed for technicians who work with dirty hands and need to stay focused on the vehicle and the customer.
Funding
Funding not disclosed
Founders
Product
Problem
Auto detailing shops struggle with existing software that requires manual input and app‑switching, which is impractical when hands are dirty and the detailer is interacting with a customer. Delays between completing a job and invoicing reduce payment collection rates, leading to lost revenue.
Solution
Servo Detail provides an AI‑powered business management platform that enables detailers to book jobs, create invoices, and manage their service catalog entirely by voice. The hands‑free interface eliminates the need to navigate multiple screens, allowing users to generate an invoice immediately after a job finishes. By streamlining workflow and reducing the time between service delivery and billing, the platform helps improve payment success rates. The solution is delivered as a web‑based application accessible from any device, with voice recognition optimized for noisy shop environments.
Target Audience
Primary customers are independent auto detailing businesses and detailing franchises that need a fast, hands‑free way to manage appointments, billing, and service offerings while interacting with customers on site.
Features
- Voice‑only commands for booking, invoicing, and catalog management, removing the need for manual data entry
- Immediate invoice generation triggered by a voice command at job completion
- Integrated service catalog that can be described and updated verbally
- Cloud‑based platform accessible from smartphones, tablets, or desktop computers
- Designed for hands‑dirty, on‑site use with noise‑robust speech recognition