The biggest shift at our team this year has been leaning hard into AI and automation.
We run these systems across our entire portfolio - $10M+/mo in ad spend across 20+ brands. They cut manual work by roughly 60%.
Before these systems, managing at this scale would have required 150+ people. Now we do it with 50. (Read that again.)
Here's the automation stack we've built.
The old way: More accounts = more people. Linear scaling. (And linear headaches.)
The new way: More accounts = smarter systems. Compounding output.
That ratio - 50 people doing what would take 150+ - is only possible because of the systems we've built.
Clients scale faster without blowing up quality. The team gets more time to think about strategy and take on more clients.
Tasks that used to eat 45 minutes now get handled with a single click. (I wish I was exaggerating.)
L.E.V.E.R. stands for: Learn (AI knowledge base + LLM acceleration), Evaluate (alerts), Visualize (competitor tracking), Execute (one-click processes), Replicate (creative at scale). These five components - implemented across six systems - work together to multiply team output.
What it does: Runs competitor research every week automatically across all active accounts.
How it works:
Why it matters: We caught a competitor launching an aggressive branded campaign against one of our clients within 48 hours. Without the alert, we'd have found out when CPCs spiked 40% and budget was already burned.
Tools we use:
What it does: Helps clients ramp YouTube and video ads fast - what used to take 2 weeks now takes 2 days.
How it works:
Why it matters: One client went from testing 3 YouTube scripts per month to 15+. Their winning ad came from script variation #12 - something they'd never have tested at the old pace.
Tools we use:
What it does: Answers any question the team has about Google Ads. (Basically a senior account manager who never sleeps.)
How it works:
Why it matters: New team members used to take 3-4 weeks to handle accounts independently. With the Brain, that's down to 10 days. The same answers our seniors would give, available 24/7.
How to build it:
What it does: Fires the second something breaks or drifts. (No more Monday morning surprises.)
How it works:
Alert types:
Why it matters: One alert caught a conversion tracking failure on a Friday night. Without it, the account would have spent $8k+ over the weekend optimizing toward broken data. That single alert paid for the entire system.
How to build it:
What it does: Everyone uses LLMs to move faster. (This is non-negotiable on our team.)
How we implement it:
Use cases:
Why it matters: We measured it. Tasks like client report drafts went from 45 minutes to 12 minutes. Keyword research expansion from 2 hours to 30 minutes. That's not a marginal improvement - it's a structural change in how many accounts one person can handle well.
Implementation:
What it does: Complex workflows reduced to single actions.
Examples:
How to build:
Why it matters: Our client onboarding checklist has 47 steps. Used to take 3 hours. Now it's 20 minutes - one click populates everything, team just validates. That's where the compounding kicks in: about 3 hours saved on every brand we onboard.
Priority 1: Alerts
Why first: Prevents costly mistakes. Immediate ROI.
Priority 2: LLM Integration
Why second: Quick productivity boost. Low setup cost.
Priority 3: Google Ads Brain
Priority 4: Competitor Tracking
Priority 5: One-Click Processes
Priority 6: AI Creative
Old thinking: "We need more people to handle more accounts."
New thinking: "We need better systems to handle more accounts per person."
The goal isn't to replace humans. It's to multiply their output. (Big difference.)
An analyst with the right automation stack manages 3x the accounts with better results than one doing everything manually.
The agencies that figure this out will outcompete the ones that don't.
When you automate the tedious stuff, you get time back. Real time. Not "theoretical 10% efficiency gains" - actual hours per week per person.
For us, it looks like this: each account manager handles 3x the accounts they did 18 months ago. Client satisfaction scores stayed the same. Response times actually improved because alerts catch problems before clients notice them.
The team spends more time on strategy, client relationships, and finding new opportunities - the work that actually grows accounts.
Everyone on the team uses LLMs to move faster and stop overthinking basic work. That's the culture shift that makes the whole system work.
The L.E.V.E.R. System exists because we made every automation mistake first:
Mistake: Building alerts without thresholds. First version alerted on everything. Team got alert fatigue within a week. Nobody checked them. Now every alert has severity levels and thresholds tuned per account size. A $5k/month account and a $500k/month account need different sensitivity.
Mistake: Creating an AI assistant nobody used. Built a beautiful internal knowledge base. Team ignored it and kept asking senior people. The problem wasn't the tool - it was the workflow. Now "ask the AI first" is mandatory before escalating. Usage went from 10% to 90%.
Mistake: Over-automating creative. Early AI creative looked obviously AI-generated. Clients pushed back. We learned that AI acceleration works for ideation and iteration, but final creative still needs human polish. The system now uses AI for drafts, humans for finishing.
Mistake: Automating before documenting. We automated broken processes. Then automated the fixes to the broken processes. Chaos. Now we document and optimize manually first, then automate only proven workflows.
The L.E.V.E.R. System is the result of every mistake above. Follow it and you skip our expensive learning curve.
This is the automation stack we run across our portfolio. It's what lets 50 people do what would otherwise require 150+. The agencies that figure this out will outcompete the ones that don't.
We put together the Google Ads script we use for account monitoring. Tracks spend anomalies, ROAS drops, and disapprovals. Takes 10 minutes to set up.
Reply "ALERT" and I'll send it over.
(Must be following)