Marketing & Growth

How to launch programmatic seo pages with AI

Marketing & Growth3 AI tools7 steps6 friction points

Want an agent to handle this on your live data? See the Starch version →

Programmatic SEO means building hundreds or thousands of landing pages from a template and a dataset — one page per keyword cluster, location, use case, or product variation. It's a core growth lever for SaaS products, marketplaces, and content businesses. The work involves keyword research, template design, content generation at scale, and a publishing pipeline that keeps pages fresh as the underlying data changes.

The workflow feels like an AI problem because so much of it is pattern-repetitive: take a keyword, slot it into a content brief, generate a page, repeat. That structure maps well to what large language models are good at — following a template, varying the output slightly per input, and doing it fast. The research layer (finding keyword clusters, analyzing SERP intent) is also the kind of synthesis work where Claude or ChatGPT genuinely saves hours compared to doing it manually.

ChatGPT, Claude, and Gemini can handle real chunks of this workflow today. They'll help you cluster keywords by intent, draft a page template with the right structural elements, and generate body copy for individual pages when you feed them a row from a spreadsheet. Where they stop is at the edges: live data, publishing pipelines, and anything that needs to run on a schedule without you re-triggering it manually.

Skip the manual work → See the Starch version

Marketing & Growth3 AI tools7 steps6 friction points

AI walkthrough

How to do it with AI today

A practical walkthrough using ChatGPT, Claude, and other off-the-shelf LLMs — what they're good at, what you'll have to do by hand.

Tools that work for this

ChatGPTClaudePerplexity

Step-by-step

1 Export your target keyword list from your SEO tool (Ahrefs, Semrush, Search Console) as a CSV, then paste the top 50-100 into Claude with a prompt asking it to cluster them by search intent and suggest a page template structure for each cluster.

2 Take your highest-priority cluster and ask ChatGPT to write a content brief: H1 format, meta description pattern, expected sections, word count, and 3-5 specific questions the page must answer for someone searching that term.

3 Build a master page template in Google Docs or Notion with placeholder variables (e.g., {keyword}, {use_case}, {location}) based on the brief. This template is what you'll feed back to the LLM for each page variant.

4 Paste the template plus a single row of your dataset into Claude and ask it to generate a complete page. Review the output, iterate on the prompt until the structure and tone are right, then lock that prompt as your production template prompt.

5 Export your full dataset as a CSV, then use ChatGPT's Code Interpreter (or write a simple script Claude helps you produce) to loop through each row, call the prompt, and write the outputs to a new column or separate files.

6 Paste a sample of generated pages back into Claude and ask it to score each one for keyword coverage, readability, and whether it actually answers the search query — use this as a QA pass before publishing.

7 Publish pages to your CMS manually or via API if your CMS supports it. Set a calendar reminder to re-run the whole chain whenever your underlying dataset changes significantly.

Prompts you can copy

Here are 80 keywords related to project management software. Cluster them by search intent into 5-8 groups. For each group, suggest a programmatic page template: what the H1 pattern should be, what sections the page needs, and what the user is actually trying to accomplish.

I'm building a programmatic SEO page for the keyword '{how to track freelancer invoices with software}'. Write a 400-word page body that directly answers the query, uses the keyword naturally in the first 100 words, and includes a clear call-to-action at the end. Tone: direct, practical, second person.

Here is my programmatic page template with placeholders: [paste template]. Here is one row of my dataset: [paste row]. Generate the complete page, replacing all placeholders with specific, non-generic content that fits the keyword intent.

Review these 5 programmatic pages I generated: [paste pages]. Score each one 1-10 on: (1) does it directly answer the search query, (2) keyword usage without stuffing, (3) whether the CTA is specific to the page topic. Flag any pages that feel too generic.

I have a CSV with 200 rows: each row has a city, a service type, and a business category. Write me a Python script that reads each row, calls the OpenAI API with this prompt template [paste template], and saves each output as a separate .txt file named by the row's slug field.

Reality check

Where this gets hard

The walkthrough above works — until your numbers change, the LLM hallucinates, or you have to re-paste everything next month.

No live connection to your content pipeline — every time you update your dataset, you re-paste rows into the chat window and re-run the prompt chain manually.

Context window limits mean you can process maybe 20-30 pages per session before you're starting a new conversation with no memory of the template decisions you made earlier.

Output structure drifts between sessions — the H1 format, section order, and CTA wording you carefully calibrated in one chat don't automatically carry into the next one unless you re-paste the full system prompt every time.

Nothing monitors page performance after publishing — you have to go back to your SEO tool separately and there's no connection between what you published and what's ranking or converting.

Generating 500 pages manually, even with a tight prompt, still means hundreds of copy-paste cycles or writing and debugging a script yourself — the LLM can help write the script, but you're still running and maintaining it.

QA is ad hoc — there's no systematic way to flag which generated pages are too thin or too generic without reviewing them one by one or building your own scoring pipeline.

Tired of the friction?

Starch runs the whole workflow on live data — no copy-paste, no hallucinated numbers, no re-prompting next month.

See the Starch version →

Starch alternative

The same workflow on Starch

Starch is an agentic operating system — for this workflow, that means an agent builds the persistent app that generates, monitors, and refreshes your programmatic pages against live business data, instead of a prompt you re-run from scratch each time your dataset changes.

Connect your analytics data through PostHog via Starch's Growth Analyst starter app — it reads your traffic and conversion data on a schedule and surfaces which programmatic pages are actually driving signups versus which clusters are dead weight, without you logging into a dashboard.

Describe your page generation pipeline in plain English and an agent builds it: 'Read my keyword dataset from Google Sheets, generate a page for each row using this template, and write the outputs back to a new sheet column.' It runs on your live data, not a one-time export.

Connect Google Sheets or Airtable from Starch's integration catalog — the agent queries your dataset live each run, so when you add 50 new keyword rows, the next scheduled run picks them up automatically without any manual re-triggering.

Build a QA dashboard in plain English: 'Show me all generated pages flagged as under 300 words or missing a CTA, with a link to each page and its current traffic.' Starch builds the view; it stays current as new pages are generated.

Starch stores your page templates, generation history, and performance data in one place using the Knowledge Management app — no more hunting through chat histories to find the prompt version that was producing the best output two weeks ago.

Automate the full loop: generate pages on a schedule, post performance summaries to Slack, and email a weekly digest via Growth Analyst that tells you which clusters are gaining traction and where to focus the next batch of pages.

Get closed-beta access →

Toolkit