AI that knows your fleet

The Problem

Technicians spend time squinting at worn dataplates, manually copying model numbers, serial numbers, capacities, and fuel types into forms. Typos happen. Fields get skipped.

What We're Building

Point your phone’s camera at a machine’s dataplate. TorqueDesk reads it and fills in the specs — model, serial number, capacity, fuel type, manufacture date. Works across manufacturers and label formats, even on plates that have seen better days.

The Problem

Technicians arrive at a site and have to piece together what’s going on. What was done last time? Are there open deficiencies? When does the safety cert expire? Is there anything the customer mentioned on the last visit? That information exists in TorqueDesk, but it takes digging.

What We're Building

Before a technician arrives on-site, the system generates a briefing. It pulls from the machine’s service history, last inspection results, known deficiencies, customer notes, and parts commonly needed for this machine type. The result is a summary that surfaces what matters: “Last PM was 400 hours ago, safety cert expires in 12 days, customer mentioned intermittent hydraulic leak on last visit.”

The Problem

Service companies are reactive. A machine breaks, a customer calls, a technician rolls. The data to predict and prevent failures exists — it’s in the work orders, the inspection results, the maintenance logs — but nobody has time to analyze it manually.

What We're Building

TorqueDesk analyzes maintenance patterns across your entire fleet. It identifies machines with recurring issues, unusual failure rates, or maintenance costs that deviate from fleet averages. It flags customers whose fleets are aging or underserviced. Instead of waiting for the emergency call, you get insights like: “3 of Customer X’s reach trucks have had hydraulic repairs in the last 6 months — consider proactive hose replacement on the remaining units.”