Tech voice memo → job summary + invoice line items + Jobber note

The problem

Owners describe doing dispatch, office and paperwork at night. We found anecdotes, not measured hours, so we don't claim a time saving.

• One-man HVAC shop owners describe being the dispatcher, office manager and paperwork person (anecdotal). Source: r/HVAC thread on running a one-man shop (Anecdote (Reddit); read via search summary, not opened directly)
• Plumbers name invoicing, scheduling and estimates as their biggest admin headache (anecdotal). Source: r/Plumbing thread: biggest admin headache (Anecdote (Reddit); read via search summary, not opened directly)

Who it's for

Owner-operators and small crews who do invoices at night, and shops whose techs hate typing on a phone.

Tools and monthly cost

Example month: Illustrative arithmetic. Assume 100 memos of about 1 minute: speech-to-text $0.30-$0.60, AI extraction under $0.05. No texting costs. Plus n8n. Or skip n8n entirely and use the included Claude skill by pasting the transcript.

Setup

1. Give techs a way to send the audio file to the workflow's upload webhook with the job id: an iPhone Shortcut or Android share target that posts multipart form data (fields: audio file, jobId, tech).
2. Create a Jobber Developer Center app with job read/write and products read scopes; connect it with an n8n OAuth2 credential.
3. Import workflow.json; set your speech-to-text and model API keys.
4. Edit the ALIASES list in 'Match price book + build draft' with the slang your techs use (e.g. 'flush' → 'Water heater flush').
5. Optional review queue: the workflow appends a row (job, items, needs-pricing, recommendations) to a sheet; swap the mock sheet URL for a Google Sheets node.
6. Record three real memos and compare the draft notes with what you'd have invoiced before letting anyone rely on it.

Download

Download voice-memo-to-invoice.zip

Contains the n8n workflow, AI prompts, a Claude skill version (documentation only, not executed in our tests), the mock data, the test runner with its last results, and the mock server we tested against. All keys in .env.example are fake.

Test result

Every workflow was imported into n8n 2.45.4 and run end to end on our test machine. Twilio, Jobber and Stripe were replaced by a local mock server that accepts the same request shapes as their documented APIs and records every call. The AI steps used a small local model (Qwen2.5-3B-Instruct, 4-bit) and the speech-to-text steps used Whisper base.en running locally. Mock data (customers, quotes, invoices, recordings) is invented. Nothing was sent to a real phone, customer or account.

Actual output from the test run

Draft note written to the mock Jobber job for memo M1 (real Whisper transcript, including its mistakes). Names and numbers are invented.

AI DRAFT from voice memo - review before invoicing

Summary: The technician diagnosed and fixed a flame failure issue on the furnace. They replaced the corroded flame sensor and cleaned the filter. The furnace is now operating normally.

Work performed:
- diagnose flame failure
- replace flame sensor
- clean filter
- replace filter
- run cycles

Suggested line items (price book prices):
- Service call / diagnostic x1 @ $89.00 = $89.00
- Flame sensor x1 @ $65.00 = $65.00
- Furnace filter 16x25x1 x1 @ $18.00 = $18.00
- Labor - per hour x1.5 @ $125.00 = $187.50
Subtotal: $359.50

Recommendations / follow-ups:
- keep an eye on the inducer motor

Transcript: "Okay, this is Job 5512, the ramen house. Furnace was locking out on flame failure. Hold the flame sensor. It was pretty corroded and pitted. So I swapped it for a new one. Filter was filthy too. Replaced it with a 16 by 25 by one. Ran it through three cycles. It's holding flame fine. I was there about an hour and a half including the diagnostic. The inducer motor is getting a little noisy. She should keep an eye on it, but I didn't touch it today."