SEO Automation
SEO Automation Services Built as Pipelines You Can Audit
I build and run the research pipelines behind my own SEO work - competitor mapping, keyword research, content gaps, briefs, drafting, internal links, link prospects and AI answer checks. You get the raw data and the reasoning, checked by me before it reaches you, rather than a dashboard score nobody can explain.
- years in SEO
- 6+
- client websites worked on
- 60+
- person doing the work
- 1
What SEO automation looks like in my work
SEO automation, the way I practise it, means writing code that carries out the slow, repetitive parts of SEO research the same way every time - pulling sitemaps, joining ranking data, crawling internal links, checking AI answers - and then putting a person in front of the output before anyone acts on it. It is not a tool subscription with a health score.
The pipelines are mine. I wrote them for my own client work, mostly in Python, with Node and Playwright for the jobs that need a real browser. Because I built them, I can change what they collect, add a check a particular site needs, or switch off a step that does not fit.
That matters because most SEO research fails quietly. A competitor's blog sits in a subfolder nobody opened. A page on the client's own site already covers the topic the new brief proposes. A directory that links to four rivals never makes the outreach list. They happen because a person checking by hand runs out of time before the list runs out of rows.
Automation fixes the coverage problem. It does not fix the judgement problem, and I do not pretend it does. The machine finds and sorts; I decide what is worth your attention.
Key takeaways
- Automation earns its place on repeatable research: the same steps run across every competitor, every page and every keyword, without the shortcuts a tired analyst takes.
- Judgement, approval and client context stay human. A pipeline proposes; I decide what goes to you.
- Every output is data you can open and check - spreadsheets, URL maps, screenshots, CSVs - not a black-box score.
- Most engagements follow the same shape: scope, build or adapt a pipeline, run it, then hand over the outputs or run it monthly.
- The pipelines are my own tooling, mostly Python, with Node and Playwright where a real browser is needed.
What's included in my SEO automation services
The service is organised around the stages a pipeline goes through, from agreeing what it should answer to running it on a schedule.
Scoping the question an automated SEO pipeline has to answer
Before any code runs, I pin down what decision the research supports, which domains count as competitors, what you already hold in sheets or tools, and your never-rules - services you do not offer, areas you do not cover, brands you will not mention. This is also where the cost of third-party data is estimated, so the size of each pull is agreed in advance.
- The research question and the decision it supports
- Competitor and market list
- Never-rules and exclusions
- Data sources and an API cost estimate
Building or adapting automation pipelines around your site and market
I start from my own pipelines and adjust them: page roles that match your site, URL patterns specific to your platform, market or locale handling, extra exclusion lists. Where a step is new, it is built and tested on a small sample before the full run, so mistakes show up in ten rows rather than ten thousand.
- Python pipelines, with Node and Playwright for browser work
- Site-specific rules and page roles
- A sample run before the full run
- Cached calls so reruns do not pay twice
Running data collection across sitemaps, rankings, crawls and AI answers
Depending on the pipeline, a run might pull competitor sitemaps and join them with Ahrefs ranking data, crawl your site link by link, scrape the pages ranking for a keyword, or put buyer prompts through AI answer surfaces in a real browser. Runs that touch rate-limited platforms are paced and can resume where they stopped.
- Sitemap, title and homepage harvests
- Ranking and referring domain data
- Site crawls with link positions tagged
- Browser runs for AI answers and local results
Human review that checks pipeline output before you see it
This is the step that separates useful automation from noise. I read the output, spot-check rows against live pages, remove anything that breaks a never-rule, and mark the decisions that need your knowledge. Keywords, topics and drafts all wait for approval; nothing is published or sent onward because a script said so.
- Spot checks against live pages
- A never-rule and relevance pass
- Questions flagged for your decision
- Approval before anything moves on
Outputs you can audit - spreadsheets, URL maps, screenshots and CSVs
You get the data itself, shaped for whoever acts on it. A designer gets a task list sorted by priority; a writer gets a brief and the competitor content behind it; you get the research sheet with sources and exclusions recorded. If you want to rerun the work in-house later, I explain how each output was produced.
- Research sheets per competitor or topic
- Priority task lists for designers and developers
- Briefs, drafts and audit results
- Notes on sources, dates and limits
Scheduled reruns that show what changed since the last pass
Some research is worth repeating: competitor sites change, new directories appear, AI answers shift from week to week. On a monthly or quarterly arrangement I rerun the agreed pipelines on fresh data, review the output to the same standard, and lead with what moved rather than resending the whole picture.
- Monthly or quarterly reruns
- A change summary against the previous run
- The same review standard every time
- Scope adjusted as the site grows
Experience
SEO Automation from someone who has done it many times
You work with me from the first call to the last report. No account managers, no hand-offs to a junior team.
Book a free call- years in SEO
- 6+
- client websites worked on
- 60+
- documented case studies
- 35
SEO automation services
Each one is a separate piece of work with its own deliverable.
Competitor Analysis Automation
Competitor sitemaps joined with Ahrefs ranking data into a URL map per rival, then rolled up into site structure, typed informational pages and backlink-gap checks. Raw rows per competitor, not a summary deck.
Read moreKeyword Research Automation
A staged pipeline from your questionnaire and approved page inventory through competitor mining, seed expansion, a relevance gate and SERP validation, with each keyword routed to a money page or the blog plan and approved by me.
Read moreContent Gap Automation
Harvests your sitemap, titles and homepage plus competitors', strips out everything live, planned or drafted, and proposes new topics that will not cannibalise existing pages. Later rounds also exclude earlier proposals and rewordings.
Read moreContent Brief Automation
Scrapes the pages ranking for a keyword, keeping accordion, tab and hero content that page builders hide, and turns it into a full competitor content document plus a brief or outline for your page.
Read moreContent Writing Automation
Drafting guided by playbooks measured from top publishers, voice profiles and an audit script that passes or fails each draft against benchmarks. A human edits and approves every page before it goes anywhere.
Read moreInternal Linking Automation
A fresh crawl from your sitemap with every link tagged by position and every page given a role, ending in a P1-P3 spreadsheet of missing links, unlinked mentions, template fixes and broken links.
Read moreLink Prospecting Automation
Finds the directories and listing sites your competitors use, checks which accept submissions and whether listing is free or paid, and ranks them by how many competitors each one links to.
Read moreAI Visibility Tracking
Runs real buyer prompts through Google AI results, Gemini, ChatGPT and Claude in a browser, saving a screenshot and answer per prompt and recording whether your brand is named or cited.
Read moreWhy automate the research behind SEO
Three reasons come up in almost every conversation about whether a pipeline is worth building.
Repeatable research
A pipeline runs the same steps in the same order on every run. When you compare this quarter's competitor map with last quarter's, the difference reflects the market, not a change in who did the work or how carefully.
More coverage
Code does not get bored on page four hundred of a sitemap. It reads every URL, every title and every referring domain, so the research reflects the whole of a competitor's site rather than the top twenty pages someone had time to open.
Fewer missed pages
Exclusion lists, cross-checks and dedupe steps catch what manual work lets slip: a topic you already cover, a keyword that belongs on an existing service page, a broken link buried in a footer template.
The eight pipelines, what goes in and what comes out
Each pipeline has its own page with the full method. This is the short version.
| Pipeline | What it works from | What you receive |
|---|---|---|
| Competitor analysis automation | Competitor XML sitemaps joined with ranking data from the Ahrefs API | A URL map per competitor, a site structure roll-up and a typed list of informational pages |
| Keyword research automation | Your questionnaire, business profile and approved page inventory | Keywords validated against live results and routed to a money page or the blog plan |
| Content gap automation | Your sitemap, titles and homepage, plus the same from competitors | New topic proposals with your live, planned and drafted work already excluded |
| Content brief automation | The pages ranking for a target keyword, including accordion and tab content | A full competitor content document and a brief or outline for your page |
| Content writing automation | Type playbooks, voice profiles and the approved brief | Drafts that pass a benchmark audit, then a human edit before anything is published |
| Internal linking automation | A fresh crawl from your sitemap, with every link tagged by position | A P1-P3 task spreadsheet for your designer or developer |
| Link prospecting automation | Competitors' referring domains, found through the search results | Directories and listing sites ranked by how many competitors they link to, with free or paid routes noted |
| AI visibility tracking | A list of real buyer prompts run through AI answer surfaces in a browser | A screenshot and answer per prompt, a gallery and a CSV of brand mentions and citations |
Pipelines can run alone or feed each other - competitor and content gap output, for example, can feed keyword research.
Free review
Get a free SEO Automation review
Send me your site. Before the call I look at your rankings and pages, then tell you what I would fix first and what it is likely worth.
A black-box SEO tool versus a pipeline you can audit
Off-the-shelf platforms are useful, and I use their data. The difference is what you can see and change.
Typical all-in-one SEO platform
- Scores and grades with the calculation hidden
- Fixed reports that treat every site the same way
- Checks limited to what the vendor chose to build
- Exports you reshape by hand before they are usable
- No record of why a page or keyword was dropped
A pipeline built for your site
- Raw rows you can filter, sort and check against the source
- Rules written for your site, your market and your never-list
- New checks added when your site needs them
- Output shaped for the person who acts on it - a writer, a designer, a developer
- Exclusions and decisions recorded alongside the data
How a pipeline engagement runs
Whether you need one pipeline or several, the work follows four stages.
- 1
Scope
First I pin down with you the question the research has to answer, which sites and competitors are in play, what you already have and what must never appear in the output. Where third-party data is involved, I estimate API costs before any large pull so nobody is surprised.
- 2
Build or adapt
Most jobs start from a pipeline I already run and need adapting - new page roles, a different market, extra exclusions. Occasionally a site needs something new, which I build and test against a small sample first.
- 3
Run and review
The pipeline does the collecting, joining and sorting. I read the output, spot-check rows against the live pages, remove what does not belong and flag anything that needs your knowledge to settle.
- 4
Hand over or repeat
You receive the outputs with notes on how they were produced. If the research is worth repeating, I run it monthly or quarterly and show what changed since the last run.
What stays human
Code proposes, a person decides
No pipeline output goes to a client unread. Judgement on what matters commercially, approval of every keyword, topic and draft, and the context only you have - which services make money, which areas you will not travel to, which claims your compliance team will not allow - stay with people. Automation removes the drudgery around those decisions; it does not make them.
What every pipeline run leaves you with
The format changes by pipeline, but the principle does not: data you can audit.
- The raw data, in a spreadsheet or CSV you can open without special software
- A note on sources, the date of the run and any limits that apply to the data
- A record of what was excluded and why, so dropped rows are not a mystery
- My review comments on the rows that need a decision from you
- Where the run repeats, a comparison with the previous one
How automation connects with the rest of my SEO work
The pipelines exist to make the SEO itself better, so they sit alongside the services rather than replacing them. Keyword research as a service is the thinking and the decisions; the keyword pipeline is the machinery that lets that thinking cover thousands of candidates instead of a few hundred.
The same split runs through the rest. A content strategy decides what a site should publish and why; the content gap, brief and writing pipelines speed up the research and drafting behind it. Link building campaigns rely on judgement and relationships; link prospecting finds the candidates. AI search optimisation is about earning mentions in AI answers; the AI visibility pipeline measures whether that is happening across a list of prompts.
If you are not sure which you need, start with the question you keep failing to answer. If it is 'what are competitors doing that we are not?', that is a pipeline job. If it is 'what should we do about it?', that is SEO services work, and the pipeline output becomes its evidence.
Growth from real projects
SEO Automation Services FAQs
What are SEO automation services?
They are research and production pipelines that do the repetitive parts of SEO with code: collecting competitor pages, mining keywords, finding content gaps, scraping ranking pages for briefs, crawling internal links, finding link prospects and checking AI answers. In my case every output is reviewed by me before it reaches you, and decisions stay with people.
Will automation replace my SEO consultant or writer?
No. Pipelines are good at collecting and sorting large amounts of data consistently. They are poor at knowing which services make you money, what your customers actually ask or whether a claim is safe to publish. The automation gives people better evidence; the people still make the calls.
Do you publish content automatically?
No. Drafts produced with help from the writing pipeline go through an audit script and then a human edit and approval. Nothing is pushed to a live site because a script finished running. The same applies to keyword lists, topic proposals and link prospects, which are all reviewed before they reach you.
Which tools and data sources do the pipelines use?
They are my own code, mostly Python, with Node and Playwright when a job needs a real browser. Data comes from sources such as XML sitemaps, live search results, site crawls and the Ahrefs API. Where a pull from a paid API is large, I estimate the cost first and agree it with you.
Can I keep the outputs and use them without you?
Yes. Outputs come as spreadsheets, CSVs, documents and screenshots that open in ordinary software, and they belong to you. If you later want to repeat the research yourself or hand it to another team, I explain how each output was produced and what its limits are.
Is automation worth it for a small site?
Sometimes not. If a site has a few dozen pages and two competitors, careful manual research is often quicker. Automation pays off when the job is large or repeated: big competitor sites, many locations or services, monthly checks, or a content plan with hundreds of candidate topics.
Talk through which SEO automation would save you the most time
Book a free call and tell me which research keeps slipping - competitor checks, keyword lists, content planning, internal links or AI answers. I will say whether a pipeline would help, which one to start with, and what you would get back from the first run.