AI-Assisted SEO Workflows
I build these for teams I am already running, not as demos. Four of them are in use: an internal linking dashboard, an overnight rank and competitor report, automated briefs and quality checks, and page structure aimed at AI answer surfaces. Built with Claude, ChatGPT and MCP.
What I have built
Four things, all taken from an idea to something a working team opens. Each one exists because a task was eating a disproportionate part of somebody's week.
Internal linking dashboard
Reads every internal link on the site and maps how link distribution actually falls, per page. Given a piece about to publish, it says which existing pages should point at it and which it should point at. Finds orphans, which no report starting from a URL list can do.
Streamlit · Google Sheets · Claude · MCP
Daily rank and competitor monitoring
Runs overnight across tracked keywords and competitor pages, then writes a short report on what moved and what to do about it. The output is recommendations the desk can act on, not a data dump they have to interpret at 9am.
Automated workflow · Claude · MCP
Content operations automation
Brief generation from SERP and query data, optimisation checks before publishing, and a helpful content quality pass that flags pages not answering the question they were written for. Advisory only, never blocking.
Claude · ChatGPT · MCP
AI search visibility
Structuring pages with schema and direct answer formats so they can be cited by AI answer surfaces, then using Search Console AI reporting to track visibility as that data becomes available. The newest of the four and the least settled.
Schema · Search Console
Why most internal SEO tools die
Almost every team has a dashboard somebody built that nobody opens. The build is rarely the reason. These six are, and I have been on the wrong side of most of them.
It lives somewhere nobody goes
A tool behind a login the team does not already have gets opened twice. Everything I have built that survived runs where the work already happens, which for content teams has usually meant Google Sheets.
It answers the builder's question
SEOs find crawl depth distribution fascinating. An editor wants to know what to write today. A dashboard that answers the first question and not the second is a dashboard that gets bookmarked and forgotten.
It blocks something
The moment a quality check can stop a piece publishing, the desk finds a way around it, and now you have both a broken process and no data. Every check I build is advisory. It is a worse tool and a far better outcome.
Nobody owns the maintenance
APIs change, sheets get renamed, someone leaves. A tool without an owner has a half life of about four months. Worth deciding who that is before building, not after it breaks.
It gives an answer with no reasoning
A recommendation a person cannot argue with is a recommendation they will not trust. Showing why a page was suggested is what turns the output from an instruction into an opinion someone can overrule, which is when it starts getting used.
It automated the wrong half
The reliable split is that machines do the gathering and people do the deciding. Tools that invert that produce a lot of output nobody asked for, and the team quietly goes back to doing it by hand.
What I work on
Working out what is worth building
Most SEO teams have two or three tasks eating a disproportionate amount of the week, and they are rarely the ones people complain about. Finding those is a shorter job than building anything and it decides whether the build is worth it.
Building it
Streamlit applications, automated reporting workflows, and integrations with the data you already have in Search Console, Sheets or a crawler. Built with Claude, ChatGPT and MCP, which is what makes a one person build realistic at all.
Connecting to your existing data
Search Console, analytics, crawl exports, rank tracking and whatever lives in the spreadsheet everyone actually uses. The integration work is usually where these projects stall, and it is not the interesting part, which is precisely why it gets skipped.
Getting it adopted
The build is the easy half. Fitting it to how the team already works, sitting with them while they use it, and changing it when they do not is the part that decides whether it exists in six months.
Content structured for AI answers
Schema markup and direct answer formats designed to be quotable by AI answer surfaces, with Search Console AI reporting used to track it. This area is moving fast and I would rather say what is uncertain about it than oversell it. Related to the technical setup.
Handing it over
Showing your team how the thing works so they can extend it, rather than leaving a black box with my name on it. Usually paired with the editorial process it sits inside.
How I build one
Five rules, the second of which is the one that matters.
Find the hours, not the annoyance
People complain about tasks they dislike, which are not always the tasks eating the week. I would rather look at where the time actually goes than at where the frustration is, because those are different lists.
Automate gathering, never deciding
Pulling, cross referencing, checking structure and surfacing patterns are jobs a machine does better and more consistently. Judging what matters is not, and every tool I have seen fail had that boundary in the wrong place.
Build it where the team already is
The best tool nobody opens is worth less than a mediocre one in a tab they already have. This constraint has shaped more of my builds than any technical consideration.
Ship something small that works
A narrow tool doing one thing well gets used and then extended by demand. A complete platform gets built for six weeks and abandoned, usually because the requirements were guesses.
Show the reasoning
Every recommendation should carry why. It is more work to build and it is the difference between a team following the tool and a team trusting it, which are not the same relationship.
"AI should take work off the team, not take the thinking away from it. Automate the gathering and leave the deciding alone."
Work I can point at
Two tools in daily use and the process change that made them worth having.
The internal linking dashboard
Maps the link graph across a site and recommends which pages should link to the piece about to be published.
Daily rank and competitor monitoring
Runs overnight, reads what moved, and writes the desk a short report with content recommendations attached.
Cutting analysis to implementation by 70%
The tools were only half of it. The rest was rebuilding how a finding reaches someone who can act on it.
Frequently Asked Questions
Something is eating a day of your team's week
Tell me what it is and I will tell you whether it is worth automating, including if the answer is no.