Hours returned:
Hours per week per role given back to the team, measured, not estimated.
//.services_006
Agentic Marketing & AI Ops
Want to see where your brand stands before we talk?
Get the baselineRoughly 85 percent of SEO teams use AI for content. About 12 percent have a documented system for it. That gap is the whole opportunity.
If your AI use is identical to your competitor's AI use, you do not have an advantage. You have a subscription. The model is the same. The difference is what you wrote down.
We install the four layers that turn scattered use into a system, then build the agents that take the repetitive work off your team's plate.
What AI Ops is
and why more tools will not fix it?
You cannot prompt your way out of an undocumented context. AI Ops is the practice of writing down the context, the process, and the quality bar so a model can actually use them.
The playbook has four layers. The first three are where most teams are missing:
Knowledge:
Brand, audience, product, and style context a model can read. Kept in version control, model-agnostic, owned by you.
Workflow and governance:
Documented steps for the work you repeat every week, with review gates and a clear answer to who can ship what.
Application:
Agents and automations applied to the highest-volume tasks first, with a 90-day plan to prove ROI before scaling.
No matter your stage of growth,
there is a program that works for your brand:
//.foundation_stage
Foundation
For startups and brands new to organic, just beginning to understand AI search. Your needs could look like this:
- Where to start, in order
- Visibility baseline across AI and classic search
- Prompt and topic strategy
- Content gaps worth filling first
//.scale_stage
Scale
For mid-market and challenger brands with some internal resources and buy-in. Your needs could look like this:
- Competitive visibility analysis
- Full content audit and plan
- Measurement framework
- Execution support alongside your team
//.enterprise_stage
Enterprise
For category leaders with multiple lines of business and large teams. Your needs could look like this:
- Cross-platform visibility measurement
- Content engineering and digital PR
- Cross-functional coordination with dev, brand, and PR
- Executive reporting tied to revenue
A knowledge layer
Brand, audience, product, and style context a model can actually read, in a repo you own.
Documented workflows
The work you repeat every week, written down, with review gates where they matter.
Governance
Who can ship what, and how quality is checked before it goes live.
Working agents
Research, briefs, QA, and reporting agents applied to the highest-volume tasks, with a 90-day ROI plan.
Watch the method before you buy it.
The 4-Layer AI Ops Playbook for SEO. SMX Advanced 2026, Boston.
Retrieved by machines.
Remembered by people.
Ranking well and being cited well are not the same skill anymore, so you should not treat them like they are. You are creating content for both humans and models now.
- How clearly your site explains what you do
- How well-structured your content is for a machine to parse
- How your brand shows up across the web, not just your domain
Machine and human understanding are not in competition. They do require different care, and you should not have to figure that out alone.
Find out where your business stands on AI search readiness:
Start with the audit ›The metrics that tell the story now
Traffic is not the full story anymore, and leadership needs measurement that reflects that. You do not need to become fluent in these yourself. You need to know someone is watching them for you and telling you what they mean for the business.
Brief to publish:
Elapsed time from brief to live page, before and after.
Defect rate:
Errors caught in review on AI-assisted output. The target is down and stable.
Documented share:
The share of recurring tasks with a written workflow a new hire could follow.
Context coverage:
How much of the brand and product context a model needs is actually written down.
Output quality:
Scored samples over time against the written standard.
Why us
This is the founder's own framework, presented at SMX Advanced and hosted as a Search Engine Journal webinar. You are buying the method from the person who wrote it.
We stay model-agnostic on purpose. Your knowledge and prompt libraries live in version control, so a better model next year is an upgrade, not a rebuild.
We prove ROI in 90 days on one workflow before anyone scales anything. Big-bang AI rollouts are how teams end up with twelve subscriptions and no system.
We organize the work in three stages:
01.
Assess
Understand where you stand today, across search and AI visibility, with a dated baseline.
02.
Prioritize
Figure out what moves the needle for your specific business. It is never one thing, and it is never everything.
03.
Activate
Put the work into motion across content, technical, and measurement, together, with your team.
//.deliverables
What we deliver
- AI Ops audit of current tools, prompts, and processes
- Knowledge base and style guide in version control
- Prompt libraries and workflow documentation
- Custom agents for research, briefs, QA, and reporting
- Team training and a 90-day rollout plan
Stay ahead of every change in AI search and SEO
The Brief is short, original, and sent when there is something worth saying.