For Leaders & Agencies
I turn your team's senior Google Ads expertise into AI agents every operator can run.
Your senior people know where spend is being wasted, which opportunities to pursue, and what standards every account should meet. I extract that expertise, fill gaps with my own 12 years of Google Ads judgment, and build custom AI agents your managers and operators use every day.
Book a free intro call →The problem
Your best Google Ads expertise is locked in your senior people's heads
Your best account managers have refined their judgment over years. They know what to look for in a search terms report, when to restructure a campaign, and which signals matter for bidding decisions. But that expertise stays in their heads.
Junior team members take 12 to 18 months to develop the same instincts. Quality is inconsistent across accounts. And when a senior person leaves, their knowledge walks out with them.
The result: your best accounts are the ones your best people manage. Everyone else's accounts perform below what your team is capable of.
SOPs and training documents capture process, not judgment. Generic AI tools produce output that still needs a senior person to fix. Neither solves the gap.
Why AI agents
Better Google Ads results across every account and every operator
Senior Google Ads judgment applied to every account
AI agents go beyond checklists. They encode the reasoning your senior people use to make decisions: when to restructure, what signals to trust, and which rules to break. Every operator gets access to that judgment.
Account specific recommendations, not generic best practices
Agents work with the specific data of each account: campaign history, conversion patterns, budget rationale. The output is precise because the context is precise.
Consistent Google Ads quality across every operator
Define your quality standards once. Agents enforce them across every account and every operator, regardless of experience level or team size. Consistency stops being a management challenge.
What managers get
Visibility into every account. Quality standards enforced automatically.
The agents I build for managers answer the questions that matter most: where is budget being wasted, where are opportunities being missed, and is every account being managed to your standards.
Surface wasted spend with one prompt
Agents scan campaigns, keywords, locations, and devices for spend that is not driving results. One command. A clear report of where money is being wasted.
Surface growth opportunities
Agents identify untapped keywords, underserved locations, and campaign gaps based on actual conversion data and account history.
Report optimization coverage
Agents measure what percentage of each account is being actively managed against your team's best practices. You see the gaps before they become problems.
Enforce a minimum quality standard
Define your standards centrally. Agents ensure every operator's work meets that bar, regardless of experience level or how long they have been on the team.
These agents give you visibility without micromanagement. You define the standard once. The agents enforce it across every account, every operator, every day.
What operators get
Every management task, guided by your team's best practices.
The agents I build for operators guide them through the work that used to depend entirely on experience and instinct. The same tasks, done faster, done better, and done to a consistent standard.
Keyword research
Agent guided keyword discovery grounded in the account's actual conversion data, not generic keyword databases.
Account structure
Agents evaluate campaign architecture against your team's standards and guide restructuring decisions.
Ad copy creation
Agents generate copy following the brand voice, proven messaging angles, and performance history of the account.
Account management
Day to day management tasks guided by agents that encode your team's standard operating procedures.
Bid strategy guidance
Agents evaluate whether current bidding approaches align with conversion goals, data volume, and business objectives.
Bid adjustments
Device, location, audience, and time of day analysis that surfaces actionable adjustments instead of raw data exports.
Operators become faster and better at every task. The time saved does not disappear into more volume. It goes into upgrading the level of how each account is managed.
The agents are defined centrally, incorporating the team's SOPs and best practices. But each operator can add context specific to their accounts. The standard is consistent. The execution accounts for the specifics of each client.
Why now
The models and tooling are ready. They were not a year ago.
AI models and tooling have reached the point where agents can provide real, actionable help on Google Ads management tasks. Not hypothetical future value. Practical value now.
A year ago, this was not possible. Earlier models lacked the context window and reasoning to make good judgment calls on complex Google Ads decisions. They could not hold enough account data or apply enough nuance to produce work worth acting on. That has changed. The agents built today can hold the context of an entire account and produce output that is ready to use.
What this is not
Not 300 agents. Not Google Ads through Slack.
This is not a library of 300 agents that will revolutionize how you do marketing. The agents built here are specific to your team's Google Ads operation. They do fewer things, but they do them well because they encode real expertise, not generic prompts.
This is not about managing Google Ads through Slack or replacing the tools your team already uses. The agents augment the management tasks your people do every day, in the environment where the work already happens.
The process
Onboarding in one week, then 2-week sprints that keep delivering
Onboarding: Assessment (Week 1)
I learn how your team currently operates: what they do well, where they lose time, and where quality varies between team members. We identify which agents to build first for managers (visibility and oversight) and for operators (guided execution), based on where AI creates the most leverage.
Sprint Week 1: Expertise Extraction and Agent Build
I work with your senior people to capture their decision-making frameworks for the prioritized agents. Those frameworks get encoded into custom AI agents using Claude Code, with my own 12 years of Google Ads expertise filling gaps. Agents are configured with centralized team standards and account-specific context so they serve both managers and operators.
Sprint Week 2: Deploy and Train
New agents are deployed to your team. Operators learn to run them for their specific accounts and provide the account context that makes output precise. Managers learn to use oversight and reporting agents. The goal is independence: your people run the agents without me being involved in day-to-day execution.
After onboarding, each 2-week sprint delivers a new set of agents. Build in week one, deploy and train in week two. Rinse and repeat. Existing agents are refined from operator feedback and changing account needs along the way.
The expertise behind the agents
Built on 12 years of results like these
The AI agents I build encode the same judgment and methodology behind these outcomes. This is the expertise your team gets access to.
Spend increase with CPL down 25%
B2B Lead Generation
Starting at $14,000 spend and $227 Cost per Lead, scaled to $55,000 a month while dropping CPL to $168. Lead volume increased from 63 to 327.
Cost per SQL reduction with flat spend
Fintech Startup
With flat spend, two restructures, and CRM-synced conversion actions, Cost per SQL dropped from over $4,600 to $1,836 while SQLs rose from 7 to 15.
"Jose has been tremendously helpful in maintaining and structuring our Google Ads accounts more efficiently. His insights and attention to detail have made all the difference in expanding and improving our overall campaign performance. We are now generating more Leads and Purchases from our B2B customers than in previous years. Working with him has made us more confident in improving performance and increasing ROI with our Paid Search campaigns."
Investment
What the engagement looks like
$5,000+/month. Cancel anytime.
The engagement includes:
- Assessment and expertise extraction
- Custom AI agent development using Claude Code
- My Google Ads expertise filling gaps in your team's knowledge
- Team training on running and maintaining agents
- Ongoing iteration and new agent deployment
- Direct access to me throughout the engagement
The starting price reflects the depth of work involved: this is not a productized package. Each engagement is scoped to your team's size, accounts, and specific gaps. Larger teams or more complex operations start higher.
Common questions
What exactly gets built during AI implementation?
Custom AI agents encoded as Claude Code skills. For managers: agents that surface wasted spend, identify growth opportunities, report optimization coverage, and enforce quality standards. For operators: agents that guide keyword research, account structure, ad copy creation, account management, bid strategy, and bid adjustments. Each agent encodes your team's decision-making standards so the output is consistent regardless of who runs it.
How long before my team can use the agents?
The first usable agents are typically ready within 2 weeks. From there we iterate based on real usage and results or keep deploying new agents at a cadence of 2 to 4 per month, depending on complexity and team adoption rate. Your team is trained to run and maintain the agents independently.
Does my team need AI or technical experience?
No prior AI experience is needed. Your team does need to be comfortable working in the Claude Code Desktop App and have basic prompting skills for cases where additional context is needed beyond the single command. The domain knowledge and judgment are encoded in the agent itself. The learning curve is minimal.
How do agents work differently for managers versus operators?
Managers get agents for oversight: surface wasted spend with one prompt, report what percentage of accounts are being optimized, and enforce quality standards across the team. Operators get agents for execution: guided keyword research, ad copy creation, account structure decisions, bid strategy evaluation. Both types of agents encode the same underlying team expertise. The difference is the interface and the output. Managers see reports. Operators get step by step guidance on the work itself.
What happens after the engagement ends?
The agents are yours. Your team has been trained to run and maintain them. If your senior people develop new frameworks or your processes evolve, you can continue building new agents internally or re-engage for additional implementation cycles.
Is this only for Google Ads teams?
Yes. Google Ads is the one thing I do, and the AI agents are built specifically for Google Ads management. This specialization is what makes the approach effective.
Why now? What changed with AI that makes this practical?
Earlier AI models lacked the context window and reasoning to handle complex Google Ads decisions reliably. They could produce surface-level output but could not hold enough account data or apply enough nuance for the output to be worth acting on. That has changed. Current models can process an entire account's data, apply the judgment frameworks encoded in the agents, and produce work that meets the standards your senior people would set. This is not about AI hype. It is about practical capability that did not exist 18 months ago.
Why don't SOPs and training documents solve this?
SOPs, training documents, and checklists capture the "what" but miss the "why." They do not encode the judgment layer: knowing when to break the rules, which metrics to ignore, and what patterns signal a real problem versus noise. They also miss account-specific context: the history behind each campaign, why budgets were set the way they are, and what has already been tested. AI agents encode both the judgment and the context, so every operator applies the same expertise regardless of experience level.
Why don't generic AI tools work for Google Ads management?
Generic AI tools do not understand your account history, your clients' bidding strategy rationale, or why campaigns were structured the way they were. They produce generic output that still needs a senior person to review, edit, and fix. The agents built here are different because they encode your team's specific decision-making frameworks and are configured with the context of each account. The output reflects your team's standards, not generic best practices.
Do the AI agents make changes directly in the accounts?
Theoretically this is possible, but there are underlying risks with automated account changes. I recommend the AI agents access the accounts through the Google Ads API with read-only permissions. The outputs are CSV files ready for import through the Google Ads Editor. This gives your team full control over what gets applied to each account.
Start a conversation
Book a free intro call
Tell me about your team, your accounts, and where you think AI could create the most leverage. No pitch. Just a conversation to see if this is the right fit.