All agents

Mobile job invoices built at the customer's yard

Builds the invoice from the tech's labor, parts, photos and signature, applies the fleet's PO and billing rules, and sends it to AP from the yard.

How it works
The reality

Billing lags the work by days.

Mobile techs write tickets in the truck, and the office types them up later. Missing POs, signatures and photos mean rejected invoices and slower cash.

A mobile tech finishes five jobs at a customer yard on Wednesday. The paper tickets reach the office Friday, and the invoices go out the next week missing two PO numbers.
A typical day for mobile fleet service
How it works

Onsite invoice closeout, step by step.

The agent does the work inside your systems. Your team approves what matters.

  1. 1

    Capture the job

    Collects labor, parts, photos and signature from the tech's phone.

  2. 2

    Check billing rules

    Matches the fleet's PO, approval limits and invoice format.

  3. 3

    Send the invoice

    Creates and sends the invoice and posts it to accounting.

What changes

Your team keeps the judgment calls.

The agent takes the repetitive work. People approve, decide and talk to customers.

  • Invoices closed out cleanly
  • Fewer rejected invoices for missing POs
  • Office staff out of ticket typing

Works inside the systems you already run

Shop managementMobile appOnsite invoice closeoutAccountingEmail
Questions we get

Before you ask.

Builds the invoice from the tech's labor, parts, photos and signature, applies the fleet's PO and billing rules, and sends it to AP from the yard. It works in three steps: capture the job, check billing rules and send the invoice.

It works inside the tools your team already uses, including shop management, mobile app, accounting and email. Nothing to rip out and nothing new to learn.

It starts in shadow mode, drafting the work for your team to approve or correct. You decide what it handles on its own, and every action is logged.

Your data is used only to run the workflows you approve. We do not use your proprietary data to train public or unrelated AI models.

Get started

Start with one agent.

Pick the workflow that hurts most. We'll scope it on the first call.