Modelling repeated job entry before automation

The problem

A firm like this has often been running for years, with a workflow that works, just not efficiently. Every new job starts with a message on WhatsApp. Someone then copies the job details into a shared spreadsheet, and when the job is complete, creates an invoice in FreeAgent by copying the same details again.

Using illustrative inputs of 15 minutes per job and 14 jobs a week gives roughly 15 hours a month to investigate. The business would confirm both inputs from a sample before relying on the estimate.

What the Blueprint found

A workflow session typically makes clear that all three tools (WhatsApp Business, Google Sheets, and FreeAgent) have API access available. The data entry happens manually only because no one has connected them. There is no technical barrier; it simply has not been set up.

The Blueprint would test this as a high-priority item: high frequency and potentially low technical complexity, with the remaining review and exception work measured rather than assumed away.

What gets built

A Make.com scenario that watches the WhatsApp Business inbox for messages containing job keywords, parses the job details, creates a new row in the spreadsheet, and drafts an invoice in FreeAgent ready to review and send. The whole scenario runs in under 30 seconds from message receipt.

You review each invoice before it goes out: the automation drafts, a human approves. That keeps you in control of what every client sees.

What changes

The hypothesis is that a connector reduces repeated entry while retaining human invoice review. Actual time impact depends on message quality, exceptions, correction work and adoption.

Measure a four-week baseline and compare it with the first four stable weeks after launch. Only that evidence can turn the illustrative monthly range into a client result.

For a real business, the first step is an Operations Blueprint: a structured review of the workflow, systems and exceptions before deciding what to build.