AI Automation

Compound output, not headcount

We automate the repetitive growth operations - lead routing, enrichment, reporting, and the handoffs between your tools - so your team spends its hours on judgment, not data entry. Outcomes you can measure: hours returned and errors removed. Not AI magic.

n8n
Durable orchestration
Human-in-loop
On every judgment call
Owned
Infrastructure, not black box
Inputs → durable core → outputs, with a person on the gate
The challenge

Your growth is capped by manual work, not strategy.

Manual handoffs, data re-entry, and disconnected tools quietly cap how fast a growth team can move. Reports get rebuilt by hand. Leads get copied between systems. Campaigns wait on someone to remember the next step.

The real questions are practical: what is safe to automate, where do you keep a human in control, and how do you avoid a brittle script that breaks the first time an API hiccups?

Re-entry tax. The same lead and campaign data keyed into three or four tools, by hand, every week.
Slow handoffs. Leads sit unrouted, reports lag a week behind, and follow-ups depend on someone remembering.
Human error. Copy-paste mistakes and missed steps that nobody catches until a number looks wrong.
Headcount creep. The only way to do more is hire more - output scales with people, not systems.
Automation isn't AI doing your job. It's infrastructure doing the busywork so people do the judgment.
// the operating principle
How we help

Automate the rules-based 70 to 85 percent. Keep people on the rest.

We map your workflows, automate the work that is repetitive and rules-based, and keep a human on every money, legal, or judgment call. The result is durable infrastructure you can see and own - not a brittle script and not a black box.

01

Workflow automation

The multi-step operations that run your week - built as durable n8n workflows that resume mid-flow instead of losing state when an API rate-limits or a network call drops. Retries, idempotency, and error routing are part of the build, not an afterthought.

n8nDurable executionError routing
02

Lead enrichment & routing

Every inbound lead enriched, scored, and routed to the right owner automatically - so nothing sits in an inbox going cold.

EnrichmentScoringRouting
03

AI content & ops pipelines

Drafting, classification, and QA steps where an AI model genuinely helps - with human review gating anything that ships.

DraftingClassificationQA gates
04

Data sync between tools

Your CRM, analytics, ad platforms, and spreadsheets kept in sync on rails we build and maintain - so the same record reads the same everywhere, and your team stops being the integration layer. This is the connective tissue that lets the rest of your growth stack compound instead of stalling at each handoff.

CRM syncTwo-waySingle source of truth
05

Reporting automation

Reports that build themselves on a schedule from live data - accurate, on time, and free of the weekly copy-paste rebuild.

ScheduledLive data
06

Internal AI agents

Where it earns its place, an orchestrated AI-agent layer that runs repeatable operations end to end, with a person owning approval.

Agent OSOrchestration
Why us

We run our own AI-agent operating system to deliver client work - orchestrated agents, automated QA, and durable workflows running every day. We sell automation from lived practice, not slideware, which means we have already hit and solved the failure modes most teams only discover in production.

Built durably

A failed step recovers. It doesn't double-charge.

Durable execution is the difference between automation you can trust with money and a script that silently breaks at 2am. Every workflow we ship carries the same backbone.

Retries with backoff on every external call
Idempotency keys so a re-run never duplicates
Error routing + alerting, not silent failure
A human gate on anything that touches money
lead-routing.workflow.json
How it works

How an automation build runs.

STEP 01

Map

We document how work actually flows today - every tool, handoff, and manual step - and quantify the hours and error risk each one carries. That map tells us what to automate first and what should stay with a person.

STEP 02

Prioritize

We rank candidates by payback and risk, then start with the high-volume, rules-based work where the time saved is fastest and the downside is lowest. Anything touching money, legal, or judgment is flagged to keep a human in the loop.

STEP 03

Build durably

We implement the workflows in n8n with retries, idempotency, and error handling so a run that fails mid-flow recovers instead of double-sending or losing state. Each automation gets an owner, alerting, and an audit trail.

STEP 04

Monitor & tune

We watch the workflows in production, catch the edge cases, and tune them as your tools and volumes change. You get infrastructure you can see and own - documented, observable, and yours to keep.

Results

Measured in hours and errors, not hype.

Build & platform partners
The whole stack

Automation is one layer of six.

It is the connective tissue. On its own it saves hours; wired into the rest of the operating system, it makes every other layer compound.

Where this layer sits

6 systems · 1 operating layer

Automation moves data between SEO, content, CRM, and analytics so the other layers compound instead of stalling at manual handoffs . We design it as part of the whole stack, not as another disconnected tool you have to wire in yourself.

FAQ

Questions buyers actually ask.

What should we automate first?
The work that is high-volume, rules-based, and low-judgment: lead routing and enrichment, reporting, data sync between tools, and content or QA checklists. We map your workflows, quantify the hours each one costs, and start where the payback is fastest and the risk is lowest. Money, legal, and relationship decisions stay with a person.
Will AI make decisions on its own?
No. We automate the 70 to 85 percent of work that is repetitive and rules-based, and we keep a human in the loop on anything involving money, legal exposure, or client judgment. Automations draft, classify, route, and sync; people approve and decide. Every automation has a clear owner and an audit trail.
Do you use n8n, Make, or custom code?
We default to n8n for workflow orchestration because it is durable, self-hostable, and inspectable, and we add custom code or AI model calls only where they earn their place. The goal is infrastructure you can see, maintain, and own - not a black box wired together with no documentation.
What happens if a workflow fails mid-run?
We build for durable execution: a workflow that stops on a network hiccup or a rate limit resumes from where it left off instead of double-charging, double-sending, or losing state. We add retries, idempotency, error routing, and alerting so a failed step is caught and recovered, not silently dropped.
Is this just chatbots and AI content?
No. Most of the value is unglamorous plumbing: moving data between tools, enriching and routing leads, generating reports, and running QA checks. We use AI models where they genuinely help - classification, drafting, extraction - and frame everything around outcomes like hours saved and errors removed, not AI for its own sake.
How do automations connect to the rest of our growth stack?
Automation is one layer of a connected operating system. It moves data between SEO, content, CRM, and analytics so the other layers compound instead of stalling at manual handoffs. We design it as part of the whole stack, not as another disconnected tool you have to wire in yourself.
Do you run AI automation internally, or just sell it?
We run our own AI-agent operating system to deliver client work - orchestrated agents, automated QA, and durable workflows. We sell automation from lived practice, not from a slide deck, which means we have already hit and solved the failure modes most teams discover in production.
Get started

Get your team out of the busywork.

Tell us where the manual handoffs hurt most. We'll map what's safe to automate, where to keep a human in control, and the hours you'd get back.