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Automation for tech content teams shipping for engineers.

Documentation that updates with the API. Blog posts that read like an engineer wrote them — because the system was built by one.

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

    Signal in

    GSC / Notion / your data

  • 02

    Claude · think + draft

    your tone, your facts

  • 03

    Publish

    WordPress, Webflow, your CMS

Why tech teams need automation.

Developer audiences smell marketing-tone content from 100 feet. They can spot AI-generated technical content even faster. Which is a problem, because dev-facing content is the highest-leverage channel a tech company has, and it's also the hardest to scale without losing the voice.

The pattern that works: dogfood your own product in the content workflow, ground every claim in actual code, treat docs as the primary content surface and the blog as the supporting cast. We build the systems that make this sustainable past the first 50 posts.

  • Docs and product are out of sync

    API ships, docs lag by sprints. Examples reference fields that no longer exist. Customers find inconsistencies before your team does.

  • Marketing-tone blog posts get ignored by your audience

    Your audience is on Hacker News, not on LinkedIn. They want technical depth, not 'leverage your engineering team's productivity.' The wrong tone tanks engagement.

  • Technical writers are expensive and slow

    Good tech writers are rarer than senior engineers and almost as expensive. Hiring more isn't the answer when shipping cadence is the bottleneck.

  • Code examples rot the moment they ship

    Dependencies update. APIs change. The example that worked at publish is broken by month two. No system to detect it, no system to fix it.

5 ways we automate tech content.

Want this built for your team?

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

Book a callBack to automation

What we use in tech.

  • n8n

    Workflow orchestration

  • Claude

    Reasoning + writing

  • Notion

    Editorial backbone

  • WordPress

    Publishing

  • OpenAI

    Reasoning + writing

  • Airtable

    Structured data

  • Ahrefs

    SEO intelligence

  • GSC

    Search Console signal

What tech teams get wrong.

  • Your developer audience smells marketing tone from 100 feet

    The phrase 'leverage' gets you instant trust loss. The same content, written like a senior engineer would explain it to a junior, gets shared. Voice matters more than topic.

  • Doc-as-marketing beats blog-as-marketing

    Stripe's docs are their best marketing. Vercel's docs are their best marketing. Your blog isn't going to be your moat — your docs might be. Invest the automation budget there first.

  • AI can write technical content. It can't write technical-feeling content. Yet.

    The structural part (outlines, examples, parameter tables) automates fine. The voice — the 'this is what an engineer who's actually shipped this thinks' part — still needs a human in the loop. Build the systems that put humans where they matter.

Four weeks from audit to handover.

  1. $ busyless audit --stack

    • Notion41 ACTIVE PAGES
    • n8n3 WORKFLOWS · 1 STALE
    • Slack12 CHANNELS MAPPED

    3 automation opportunities ranked.

    Step 01 · Week 1

    Audit + mapping

    Stack review. We map your content ops end-to-end and lock the one automation that pays back the fastest.

  2. $ busyless build --system

    • TriggerNOTION DB
    • AgentCLAUDE · RAG
    • ApprovalSLACK
    • PublishWEBFLOW

    Live on staging.

    Step 02 · Week 2

    Build

    I ship the system on n8n + Claude + your CMS. You watch the Loom; we review checkpoints every 48h.

  3. $ busyless test --quality

    • researchPASS
    • draftPASS
    • publishPASS

    0 errors. 12 edge cases caught.

    Step 03 · Week 3

    Test

    Quality gate, edge cases, and a hand-off run with one of your editors. Anything off gets fixed inside the window.

  4. $ busyless handover --doc

    • Loom12 MIN WALKTHROUGH
    • Runbook24 SECTIONS
    • Credentials1PASSWORD

    2 weeks support included.

    Step 04 · Week 4

    Handover

    Workflow JSON + docs + a recorded walkthrough. You own the code; I'm one Slack away.

One operator beats the alternatives on every axis that matters.

No account managers, no junior hand-offs, no 9-month onboarding. The person scoping the build is the one shipping it.

Busyless
Agency
Full-time hire
DIY
Time to first system
14 days
6–10 weeks
3–6 months
Whenever
You own the code
Yes
No
Yes
Yes
Per-automation cost
$2,250
$25k+
$120k+/yr
Your time
Account managers
No
Yes
No
No
Stack depth (n8n / Claude / AirOps)
Native
Outsourced
Hiring lottery
DIY
Ongoing maintenance
Optional
Retainer
Salary
Yours
Walks away if it isn't working
Yes
No
No
—

Need something the table doesn't cover?

Custom scope, retainer, or a one-off prototype — say what you need on the call.

Talk to me

Pricing

Three ways in. All priced upfront.

  • Audit

    $1,500one-off

    Find the bottleneck. Get a 30-day automation roadmap.

    Start with an audit

  • Build

    $2,250per automation

    One custom automation, shipped in 14 days.

    Scope a build

  • Fractional

    $7,650per month

    Content marketing strategy + automation, monthly.

    Book a strategy call
Need the long-term retainer or the lightweight mentorship option? See full pricing

Map automation across your tech stack.

30 min. We walk your content ops, lock the bottleneck, and pick the one tech automation that pays back fastest. No qualification form.

Direct calendar

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One sentence on the bottleneck. I'll reply within 24h with a sharper next step.

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Frequently asked

  • Will it work with our docs platform?
    Yes for Docusaurus, Mintlify, ReadMe, GitBook, Nextra, and most markdown-based stacks. The workflow pushes through your normal CI pipeline — no doc-platform lock-in.
  • How do you keep code examples accurate?
    The pipeline runs every code example against the current API on a schedule. Failed examples get flagged, an AI-drafted fix gets proposed, and a human approves before re-publish. The 'rotting examples' problem becomes a Slack alert.
  • Can the AI write in our voice?
    Yes — we train on your existing best posts and your engineering team's writing samples. Output goes through a tone-check before publishing. Most tech teams we work with end up with a system that sounds more like them than their previous human-only writers, because the system learns from the best posts and ignores the bad ones.