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AI automation: automation systems for content teams.

Claude-powered workflows that do the thinking — content enrichment, optimization, competitive research, programmatic landing pages. Production-grade, not prototype.

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  • 57+projects shipped
  • 2.5M+organic traffic managed
  • 14 dayskickoff to live system
workflows/ai-automation.n8nRunning
  • 01

    Signal in

    GSC / Notion / your data

  • 02

    Claude · think + draft

    your tone, your facts

  • 03

    Publish

    WordPress, Webflow, your CMS

What you get under AI automation.

Most AI automation in 2026 is still demo-grade. A prompt strapped to a webhook. No QC gates. No hallucination scans. No fallbacks when the model is down. We see the bills, then we see the embarrassing posts, and then we see the rollbacks.

Production-grade AI automation looks different: quality checks on every output, human approval gates where stakes are high, retry logic when models fail, observability so you know what shipped and why. That's the bar we build to.

  • Production-grade, not prototype

    Quality gates, hallucination scans, fact-check steps before output ships. Built for content teams that can't roll back a viral mistake.

  • Trained on your context

    RAG over your docs, brand voice guidelines, internal knowledge. The model knows your domain. The output sounds like you, not the average of the internet.

  • Compounds with your data

    Every output improves the next. Feedback loops fine-tune prompts on what works for your audience. The system gets sharper, not stale.

  • No vendor lock-in

    n8n + Claude API. You own the workflow JSON. We hand it over with full documentation. Swap models when better ones ship.

What we ship under AI automation.

  • Live

    Programmatic

    Programmatic SEO AI

    Spin up 100–10,000 templated landing pages from a structured data source with AI-generated copy.

    n8nClaudeAirtable+1
    HighSaves 40h+/mo2+ weeks
  • Live

    SEO

    AI SEO workflow

    End-to-end SEO pipeline: keyword → outline → draft → optimization → publish, with a human approval gate.

    n8nClaudeNotion+1
    HighSaves 40h+/mo2+ weeks
  • Available

    Product

    Product content enrichment

    Pull product specs, reviews, and competitor data into one source-of-truth feed for every PDP.

    n8nClaudeAirtable+1
    MediumSaves 10–40h/mo1–2 weeks
  • Available

    Product

    Product content optimization

    Continuously rewrite PDPs and category pages from search-intent shifts and conversion signals.

    n8nClaudeAirtable+1
    HighSaves 40h+/mo2+ weeks
  • Available

    SEO

    SEO competitor research

    Track competitor publishing cadence, ranking shifts, and content gaps automatically.

    n8nAhrefsApify+1
    MediumSaves 10–40h/mo1–2 weeks

Want this built for your team?

Book a call and walk through what we'd adapt for your stack.

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AI automation for any industry.

Talk through your AI automation roadmap.

30 min. We map your stack, find the highest-ROI ai automation automation, and ship a plan you can act on — whether we work together or not.

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Frequently asked

  • Why Claude over GPT?
    Claude is better at following instructions, less likely to hallucinate, and handles long context better — all of which matter when you're building production content systems. We use other models where they win (Cohere for embeddings, Whisper for audio) but Claude is the default for content workflows.
  • What happens if Claude changes its API?
    All our workflows abstract the model call through n8n. Swapping models means changing one node. We've already migrated client workflows from GPT-4 → Opus 3 → Sonnet 4 → Opus 4 without breaking anything.
  • Can it work with my proprietary data?
    Yes — we set up RAG (retrieval-augmented generation) over your internal docs, knowledge base, or product catalog. Data stays in your infra; only the relevant chunks go to Claude per request.
  • How do you prevent hallucinations?
    Multi-layered: temperature settings tuned per task, retrieval-grounded prompts (the model is forced to cite what's in context), output validation against schema and source documents, and a human review gate for anything that crosses a confidence threshold.