The AI Automation Playbook

The exact frameworks our team uses to design, build, and ship AI automations that save clients 1,000+ hours a year.

The problem

Why most automation projects fail

Most teams automate the wrong thing first. They optimize a sub-step instead of replacing the workflow. The result: faster broken process.

We start by mapping the full workflow (every input, every decision, every handoff) and identify the one chokepoint where automation compounds.

The framework

Clean. Build. Run.

Three phases, run quarterly. Each phase has a deliverable and a measurable outcome.

• CleanAudit + delete. Most workflows shrink 30-40% before any automation.

Clean

Audit + delete. Most workflows shrink 30-40% before any automation.

• BuildReplace manual handoffs with agents, webhooks, and one shared source of truth.

Build

Replace manual handoffs with agents, webhooks, and one shared source of truth.

• RunMonitor with alerts, retros, and a quarterly clean cycle. No system stays clean forever.

Run

Monitor with alerts, retros, and a quarterly clean cycle. No system stays clean forever.

Real example

How we cut a 6-step process to 2

A 15-person growth agency used 7 tools to qualify a lead. We mapped it, killed 5 tools, and replaced 4 manual handoffs with a single n8n flow + Claude agent.

Lead-to-qualified time dropped from 4 days to 11 minutes. Same team, same volume, more output. This is the same custom workflow automation process we run for every client engagement.

Tools we use

• nn8nWorkflow glue between every tool

n8n

Workflow glue between every tool

• CClaude CodeAgent that handles scoring + drafting

Claude Code

Agent that handles scoring + drafting

• CClayLead enrichment at scale

Clay

Lead enrichment at scale

• HHubSpotSingle source of truth for pipeline

HubSpot

Single source of truth for pipeline

• SSlackReal-time alerts + approvals

Slack

Real-time alerts + approvals

Need help with this?

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