How I do SEO at Optimal
Since May 2026 I've been a Senior SEO Manager at Optimal, a OneMagnify company, working on SEO and AI visibility for enterprise brands across many industries. I love the hard problems. Below is how I work, start to finish.
Where this work sits on the timeline, with dates
- OneMagnify acquired Optimal's performance marketing business in February 2026, a few months before I joined. OneMagnify's announcement
- Optimal has an office here in Austin, where I live.
What I get to do at Optimal
Every search starts with someone trying to decide something, and my job at Optimal is getting them the right answer. Sometimes they type it into Google. More and more, they ask an AI. I get to do this across industries that all work differently, right as search changes every day.
I love the hard problems. I dig into the content and the architecture, figure out what's really going on, then explain it to the client in a way that makes sense. My clients are all over the map, and they push me. I'd rather be in the ring. I fail at stuff, I learn from it, and the next win comes faster.
Where you can check this
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Industries that all work differently How the method changes by industry: who searches, the steps and the tools. How SEO changes by industry
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Explaining it so it makes sense How I work with clients, and the rules every report I send is held to. How I work with clients
Every engagement gets the same three things from me: a clear way to think about the problem, a repeatable way to get it done, and a written memory, so the next round starts where the last one finished instead of from scratch.
I use AI for the research, the data pulls and the repetitive build work, and to argue against my ideas before a client does. I decide what should exist and why, and I set the standards. Every number gets verified, every deliverable gets red-teamed, and every mistake becomes a rule.
Why I keep deciding and building apart
I separate decisions, execution and memory so each can be checked.
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Thinking
Strategy, prioritization and stress-testing. What pages should exist, what intent they serve, what the client actually needs to decide. Every idea gets argued against before a client sees it, so the weak spots show up early.
More on this: Priorities by business, User intent strategy
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Execution
Build scripts, data pipelines, audits, decks, workbooks, schema and content drafts, built to an architecture I've already decided. The rule: when execution needs a new strategic decision, stop and go back to the thinking layer.
More on this: Site architecture, Structured data and entities
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Memory
Every engagement has written project instructions, a decision log, standards and a list of known mistakes. Each working session starts from that context, so the work picks up where it left off instead of starting over.
More on this: Trust and sources, Using data to improve the page
People rotate, accounts change hands, and decisions get made in meetings that never get written down. A memory layer means the program survives all of that.
What I do first on every account
I name the searcher's decision before choosing a page, fix or report.
The four map onto my SEO system.
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Start from the searcher's intent
Before any page, report or fix, I name who is searching, what they typed and what they need to do next. Every other choice on the account answers to that.
Get understood and get chosen.
More on this: User intent strategy, Answer-first content, The path to a decision
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Make the site easy for search and AI to find and read
Clean crawling and indexing, a structure that matches how people search, and fixes made at the template so one change reaches every page built on it. The right answer on the site does nothing for a searcher who never sees it.
Get found.
More on this: Crawl and index health, Site architecture, Internal link strategy
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Measure what searchers did after the change
A baseline before the work, the same report structure every month, and AI visibility broken out by platform and by kind of buyer, so "it worked" always comes with a number someone else can check.
The measurement layer, under all three outcomes.
More on this: Using data to improve the page
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Work with the teams who ship it
Digital PR, paid search, analytics and the client's own developers. A plan SEO writes alone ships slowly or not at all, so they're part of it before it's set, and the pages PR pitches are ready for the attention they earn.
Get chosen.
More on this: Digital PR and mentions, Priorities by business
What the work looks like week to week
I start with repeatable changes that improve more than one page.
Start with the template
On a big site, one template sits behind thousands of pages. One careful change to its titles, headings, internal links or structured data lifts every one of them at once, so that's where I start. Technical SEO on complex websites
Give the pages that matter something no one can copy
Then I pick the pages that carry the most weight and add what a competitor can't scrape: first-party detail, the policies as written and answers from the people who run the business. That's the moat.
Product pages that answer the buying questions
Product page templates get the details shoppers check before they buy, in the order they check them, with product structured data underneath. One template change, every product.
FAQs from questions people asked, never invented ones
FAQ sections come from what customers actually ask: search queries, People Also Ask, support conversations. If nobody asks it, it doesn't go on the page, and it never goes into schema just to fill a slot.
Monthly reports on one fixed structure
The data pull and the comparisons are automated, with every number traced to its source. I write what the numbers mean and what we do next.
How SEO changes with what people are deciding
One site can serve several decisions. Each needs a different answer.
The page types behind each are in the four types of search intent.
A big purchase people research for weeks
They compare, read reviews and look for the catch. The pages that win are honest comparisons, reviews from buyers and a straight answer to the question they'd be embarrassed to ask a salesperson. What is commercial search intent?
A quick buy from a big catalog
They know roughly what they want and need to find it fast. Templates, filters and product structured data do most of the work; the copy's job is to settle the last doubt.
A service people need now, nearby
Location, hours and availability decide it. Location pages, business profiles and the same details everywhere on the web matter more than any blog post. Why local SEO is underrated
A professional or regulated service
Trust decides it. Plain-language explanations, named credentials and a review step for every claim, because a wrong fact here costs more than a lost click.
A brand fans already love
Fans arrive with specific questions: release dates, editions, what's next. Answer those first and fast, and stay honest about what's confirmed. My fan sites run on exactly this.
How I get SEO work live in a big company
A sound fix matters only when it survives review and reaches the site.
Make the ask small enough to approve
A template change with a clear before and after gets a yes faster than a sitewide overhaul. Big recommendations get broken into changes a reviewer can approve in one meeting. Repeatable SEO systems for site launches
Hand over work that's ready to build
Every recommendation says what to change, where, and how we'll know it worked, so it can go straight into a development ticket instead of waiting for someone to translate it.
Plan around review cycles, not against them
Work that needs brand or legal review starts first. Quick technical fixes fill the time while it's in review, so the calendar rarely sits empty.
Keep stalled work visible
Anything waiting on an approval shows up in the report with what it's waiting on, so a delay is a known cost instead of a quiet one.
How I work with clients
I write for the person who must decide what happens next.
Simple beats impressive
Complex SEO and AI search ideas get translated into plain language and one clear recommendation, so the client can explain it upward without me in the room.
Agendas built from what they asked
Before each meeting I go back through what they asked for and what is still open, so the agenda starts with their priorities, not mine.
Reports that answer "so what"
Every report leads with what changed, why it matters to the business and what happens next. The data is there for anyone who wants it; the story comes first.
Give more than was asked
The ready-to-build ticket, the one-slide summary, the answer before the follow-up question. The goal on every account is to make the client's week easier.
The tools behind the work, one job each
Two tools do the setup and the research, so my week goes to the recommendation.
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Peec AI: set up the AI visibility project
A new AI visibility project starts with the questions people in that market ask AI tools, tracked every day, with the competitors the answers name.
From Peec AI's quickstart guide, read Oct 14, 2026.
My part: I write the question set and freeze it, so each month compares like with like.
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Ahrefs: set up the site project, then dig into one question
The Ahrefs project watches the site's crawl health and Search Console data. When a number moves, Site Explorer and Brand Radar are where I dig into why.
From Ahrefs on projects (Sep 3, 2026) and Ahrefs on Brand Radar (Sep 3, 2026), read Oct 14, 2026.
My part: The deep dive ends in a recommendation and the number that would show it worked.
Patterns I reuse at work
I keep the standards in reusable files so the next project starts further ahead.
Each pattern sets the standard and the checks; I make the calls on what comes back.
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Monthly SEO and AI visibility reporting
The problem: Monthly reports drift. Numbers get pulled from different windows, sources get blended, and nobody can trace a figure back to where it came from.
It starts with the client's north star goals and works down to what can be tracked page by page, in organic search and in AI answers, so each number connects to a goal. I use page-level baselines and repeatable monthly reporting to compare the work over time. Shipped changes, search visibility, citations and what people did on the site stay separate. Each number needs a source and a reporting window. The aim is a report the client can follow each month, with changes they can trace and limits they can understand.
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Technical SEO and GEO audit
The problem: Audits that flag everything as broken lose credibility fast, because some of what looks wrong was a deliberate choice.
Opinionated, evidence-backed recommendations a team can actually act on.
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AI visibility prompt strategy
The problem: Brands want to know how they show up in ChatGPT, Gemini and AI Overviews, but tracking the wrong questions produces confident-looking noise.
Measurement design before measurement. When the question set moves, the trend line means nothing.
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Content built for search and answer engines
The problem: A content calendar can fill every slot and still not move visibility.
Content as infrastructure. A page earns its place on the calendar by filling a gap I can show: searches in Search Console that grew or slipped month over month, quarter over quarter and year over year; questions where Ahrefs and Peec AI show others getting found and cited and the brand missing; and what people say on Reddit that no page answers yet, or answers wrong. AI sorts that much data quickly, and I pick which gap gets a page and what it has to change. Then each page has a job, a place in the architecture and a reason to be cited.
How I track results
Each claim in the report needs a source and a decision it can support.
- Every number has a source. Source, window, scope and raw count, logged before it's used.
- Say what the number covers. "18 of 30 pages with data," not just "60%."
- Never blend what shouldn't be blended. Different engines, different windows, different sources stay separate.
- Retrieved is not cited, and cited is not chosen. The words mean different things and stay that way.
- Search data decides the KPI. AI visibility is diagnostic. It tells you where to look, not whether you won.
- Baselines are fixed. Pick a start date, freeze the question set, compare against it.
- Credit honestly. Check who built a page before claiming the result.
- Weak data stays out of the big room. Unverified numbers get a one-on-one conversation, not a slide.
Rules I work by
- Context first. A better project file improves every prompt that reads it.
- Fix the source, never the output. One upstream change reaches every page built from it.
- Every mistake becomes a rule. Written down and carried forward, so it's much less likely to catch me twice.
- Verify, don't trust. Tool labels and my own first pass get checked against the source like everything else.
- Red-team before it goes external. Stale links, stray comments, unverified claims.
- Finish small before going wide. Depth earns trust from people and search systems.
- Know which document is the source of truth. Everything else is context.
- Audience first. What belongs in this conversation, with this person, in this format?
Where you can see the work
The decisions change with the industry. The field guide shows what I look for, what I build and how I check the result in six kinds of site, with a story from my client work in 4 of them, the client kept anonymous.
This is my personal site. What I write here is my own view, not Optimal's or OneMagnify's.