Ai-powered, Expert-led
Once a Google Ads account has been built, optimized, and stabilized, the work changes.
The strategic decisions become less frequent, but the account still requires ongoing monitoring, analysis, and maintenance—work that can consume specialist time without necessarily requiring specialist judgment at every step.
AI Can Do More Than Automate Marketing Tasks
AI-powered, Expert-led
Your specialist shouldn’t have to do all the routine work
The opportunity isn’t to replace the Google Ads expert.
It’s to determine how much of the routine process—gathering information, evaluating performance, identifying changes, and preparing recommendations—can be handled by AI while keeping judgment and control with the specialist.
That was the question behind Cadence.
Cadence explores a different model for PPC maintenance
Cadence is a functional prototype designed to combine live campaign data with persistent client context, a defined diagnostic process, and rules governing how AI evaluates the account.
Rather than simply giving campaign data to an AI and asking what it thinks, Cadence provides the context the system needs to reason inside the business.
Understands the client objective, KPIs, budget, and campaign context
Evaluates performance using a defined account-maintenance framework
Identifies what is happening and recommends what should happen next
Checks recommendations against established rules and constraints
For the prototype, Cadence stops at recommendation. It does not make changes to the advertising account.
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See Cadence In Practice
A short demonstration of the prototype, how it evaluates a Google Ads account, the context and rules that guide its reasoning, and how it turns campaign data into evidence-based recommendations.
Cadence began with an informal conversation with an agency leader about whether AI could reduce the time and cost of maintaining mature Google Ads accounts without sacrificing quality or control.
The Method: From conversation to working prototype in days
From Conversation to Functional Prototype
I translated that conversation into an initial specification and used AI as a research, design, and coding accelerator to build the first working prototype in under a day.
Over the next few days, the prototype was refined enough to surface questions and requirements that weren’t obvious in the original conversation.
Instead of continuing to speculate about what might work, there was now something tangible to test.
The prototype is part of the discovery process
Fast prototyping replaces assumptions with evidence.
A functional prototype gives the people who would actually use the system something they can interact with, challenge, and evaluate. That moves the conversation beyond theoretical recommendations and projected outcomes toward direct experience.
The next step for Cadence is to test it against the original question:
Can it meaningfully reduce the effort and cost required to maintain mature PPC accounts while preserving account quality, expert judgment, and control?