22+ automation services replacing a tangle of no-code workflows.
Operational automations had grown into ~10 fragile no-code workflows that failed silently. I migrated them into one owned Python codebase and extended it, with the plumbing that makes alerts trustworthy.
Operational automations had grown organically in a no-code tool: scattered workflows, no shared deduplication, silent failures nobody caught until something broke. Nobody trusted them, so people worked around them by hand. Meanwhile the processes that actually move revenue, chasing expired callbacks, rescuing cancellations, escalating untouched leads, still depended on people remembering to check lists.
A single Python automation service, migrated one-for-one from the no-code sprawl and then extended to 22+ services. The ones that matter most:
- Speed-to-lead guardrails: every fresh appointment-lead untouched after 15 minutes alerts its owner; after 45 minutes it escalates to management; a one-click handoff broadcasts the lead to all closers, first claim wins.
- Expired-callback recovery: a daily per-agent list of overdue promised callbacks with a three-step button flow, so promises to prospects stop evaporating.
- Cancellation-retention lifecycle: realtime alerts when a save-call is due, auto-updating status, end-of-day count of unhandled cases. This alone replaced three no-code workflows.
- Self-service flows: a slash command that pushes a hot lead to every closer first-claim-wins, ticket intake straight into the task system, call-time checkpoints four times a day.
- The boring plumbing: persistent deduplication, overlap protection and rate-limit handling. That's what makes alerts trustworthy enough that people act on them.
One maintainable codebase instead of a black box of no-code sprawl. Versioned, debuggable, extended in hours instead of days. Alerts people trust. Overdue leads surface in minutes with an owner and an escalation path, and managers run their day from alerts and buttons instead of spreadsheets and memory.
An XGBoost model trained on 295k leads and 528k calls ranks any purchased list before the first dial.
- Live A/B test on 28,695 leads: 0.6% vs 0.1% appointments, same agents, same scripts
- +18% reach, +53% longer conversations
- Scores from public data only, so any fresh list can be ranked before dialing
The layer that assembles, checks and delivers the numbers daily, without anyone lifting a finger.
- 4 scheduled daily briefings straight from the warehouse
- AI watchdogs on a cadence: KPI analyst, workflow watchdog, data-quality checks
- One surfaced that 50-64% of quotes had no lead-source attribution
This same approach works on any operation drowning in disconnected workflows nobody dares to touch.