How Acme Corp replaced three tools with Flowpath and saved 20 hours per person per week

When Sarah Kim joined Acme Corp as VP of Operations in January, she inherited a automation stack that had grown organically over four years. Nobody had designed it — it had accumulated, one tool at a time, as each department solved its own problems independently. The result was 23 active Zapier workflows, 7 Make scenarios, a Python script running on a cron job in AWS Lambda, and a shared Google Sheet that three different automations wrote to and read from simultaneously.

The scariest part," Sarah told us, "was that nobody on my team could tell me with confidence exactly what all of it did. There was one workflow that had been running for two years and nobody knew what would happen if we turned it off.

The audit

Sarah's first week was spent mapping the existing stack. She categorized every workflow by three criteria: business criticality (would the business break without it?), complexity (how many steps and conditions?), and ownership (does anyone actively maintain this?).

The findings were illuminating. Of the 30+ automations, 8 were business-critical, 14 were moderately important, and 9 were running but serving no clear current purpose. The Python script — the one nobody understood — turned out to be the most critical thing in the stack: a lead scoring function that ran every 15 minutes and updated HubSpot contact scores based on behavioral data from Intercom.

The migration: three phases

Phase one was the lead pipeline. Sarah rebuilt the Python scoring function as a Flowpath AI step in four hours — including testing. The natural language prompt approach meant she could encode the same scoring logic without writing a single line of code, and the resulting workflow was readable by anyone on her team. She ran it in parallel with the Python script for a week, comparing outputs. The Flowpath version matched the Python version on 97% of leads — the 3% discrepancy was in edge cases where the Python script had hardcoded company names that no longer existed.

Phase two was the client onboarding sequence — a 12-step Zapier workflow that had been modified so many times it had lost its original logic. Rather than migrate it step by step, Sarah rebuilt it from scratch in Flowpath using a clearer structure with proper branching for the three different client types they serve: SMB, mid-market and enterprise. The rebuild took three hours and produced a workflow that was demonstrably better than what it replaced.

Phase three was the Make invoice processing flow. This was the most complex migration — Make's visual interface had allowed the previous owner to build something that worked but was architecturally messy. Three separate Make scenarios handled standard invoices, amended invoices and credit notes. In Flowpath, Sarah collapsed all three into a single workflow with a Branch node at the top that routes based on invoice type. The result is one workflow that's easier to understand, easier to maintain and significantly easier to debug when something goes wrong.

The results

Thirty days after the final migration, Sarah shared her metrics with us. Zero broken workflows in the first month — compared to an average of 2.3 incidents per month under the old stack. Manual ops work reduced by 90%, measured by tracking the number of times team members had to intervene in automated processes. The combined Flowpath bill was 40% cheaper than the three tools it replaced. And the part-time engineer who had been maintaining the Lambda function and the Make scenarios was fully reassigned to product infrastructure work.

The thing that surprised me most," Sarah said, "was how quickly my non-technical team members started building their own workflows. In the old setup, any new automation required going through engineering. Now our support lead built her own ticket triage workflow in an afternoon. That's the real value — it's not just that the automations work better, it's that more people can own them.