How to Build an AI Content Pipeline with N8N and Hono

Most content teams hit the same wall: writing isn't the bottleneck—it's everything around it. Research, outlining, QA, formatting, publishing. An AI pipeline doesn't just speed up drafts; it makes the whole process repeatable.

This guide shows you how to wire up n8n (workflow orchestration) and Hono (lightweight API) into a production system. We'll build something you can actually ship, not a demo that falls apart under real load.

What makes a content pipeline "production-ready"

Before touching code, get clear on what production means here:

• Observable: You can see what failed and why
• Resilient: Retries, fallbacks, manual overrides
• Modular: Swap LLM providers without rewriting everything
• Secure: API keys in secrets, rate limits enforced

n8n handles orchestration well—branching, retries, scheduling. Hono is fast and runs anywhere (Node, Bun, edge). Together they cover the hard parts without overengineering.

Architecture overview

The pipeline moves through seven stages:

1. Trigger — Schedule, webhook, or manual kick-off
2. Brief — Topic, audience, angle, constraints
3. Research — Pull sources, extract key points
4. Draft — LLM generates content from outline
5. QA — Word count, citations, banned phrases
6. Enhance — Internal links, CTAs, metadata
7. Publish — Push to CMS or Git

n8n runs the workflow graph. Hono provides specialized endpoints for research, linking, and validation.

Step 1: Define your content schema

Start with the data model. A clean schema prevents brittle automations:

interface ContentJob {
  id: string
  topic: string
  audience: string
  intent: "[informational](https://revenue-boost.app/blog/email-capture-on-shopify-without-hurting-your-brand-7-popup-patterns-customers-don-t-hate-2025)" | "transactional"
  keywords: string[]
  outline: Section[]
  sources: Source[]
  draft: string
  meta: {
    title: string
    description: string
    slug: string
  }
  status: "brief" | "research" | "draft" | "review" | "published"
}

interface Source {
  url: string
  title: string
  snippet: string
  trustScore: number
}

Store this in Postgres, Airtable, or a Git repo. The key: every pipeline stage reads and writes predictable fields.

Step 2: Build the Hono API

Hono provides endpoints for tasks that don't belong in n8n nodes—structured retrieval, link suggestions, validation rules.

import { Hono } from "hono"

const app = new Hono()

// Research endpoint - returns sources for a topic
app.post("/research", async (c) => {
  const { topic, keywords } = await c.req.json()
  
  // Call Brave Search or your vector store
  const sources = await fetchSources(topic, keywords)
  
  return c.json({
    sources: sources.slice(0, 5),
    keyPoints: extractKeyPoints(sources)
  })
})

// Internal linking endpoint
app.post("/links", async (c) => {
  const { content, maxLinks = 5 } = await c.req.json()
  
  const suggestions = await findInternalLinks(content)
  
  return c.json({
    links: suggestions.slice(0, maxLinks)
  })
})

// Validation endpoint
app.post("/validate", async (c) => {
  const { content } = await c.req.json()
  
  const issues = []
  const wordCount = content.split(/\s+/).length
  
  if (wordCount < 1200) issues.push({ type: "warning", message: "Under 1200 words" })
  if (!content.includes("## ")) issues.push({ type: "error", message: "Missing H2 headings" })
  if (/game-changer|leverage|synergy/i.test(content)) {
    issues.push({ type: "warning", message: "AI-isms detected" })
  }
  
  return c.json({ valid: issues.filter(i => i.type === "error").length === 0, issues })
})

export default app

Keep endpoints stateless so n8n can retry safely.

Step 3: Wire the n8n workflow

n8n's visual builder handles the orchestration. Here's the node structure:

Trigger → Cron or webhook HTTP Request → POST /research to Hono AI Node → Generate outline from research AI Node → Generate draft from outline HTTP Request → POST /validate to Hono IF Node → Gate on validation errors HTTP Request → POST /links to Hono Function Node → Inject links into content HTTP Request → Publish to CMS

The n8n docs have a solid AI workflow tutorial if you're new to their LLM nodes.

Step 4: Internal linking service

Internal links are tedious to manage manually but easy to automate. Store an index of posts with focus keywords:

interface PostIndex {
  slug: string
  title: string
  keywords: string[]
}

async function findInternalLinks(content: string): Promise<LinkSuggestion[]> {
  const posts = await db.posts.findMany()
  const contentLower = content.toLowerCase()
  
  return posts
    .filter(post => post.keywords.some(kw => contentLower.includes(kw)))
    .map(post => ({
      anchor: post.keywords.find(kw => contentLower.includes(kw)),
      url: `/blog/${post.slug}`,
      title: post.title
    }))
    .slice(0, 5)
}

Call /links after drafting and inject suggestions. For Shopify-related content, you might link to resources like popup strategy guides or email capture tips when they're contextually relevant.

Step 5: Quality gates

Automation without guardrails ships garbage. Add checks that stop or flag bad output:

• Word count within range (1200-2500)
• Required H2 headings present
• No missing source citations
• No banned phrases (compliance, brand terms)
• Keyword density not excessive

n8n's IF nodes make gating easy. Centralize complex checks in your Hono /validate endpoint.

Step 6: Publishing and feedback

Once content passes QA, push to your CMS or commit to a Git repo. Store metadata for learning:

• Human edit diffs (what did editors change?)
• Performance data (CTR, time on page)
• Revision history

This feedback loop is where automation compounds. You learn which prompts produce content that editors don't touch, and which sources actually drive quality.

Step 7: Deployment

Hono runs on Node, Bun, or serverless. A typical setup:

• Hono API on Bun (fast, cheap)
• n8n self-hosted or cloud
• Postgres for content storage
• CMS (headless or static site)

Lock down API access. Use tokens or IP allowlists—these endpoints can trigger content creation.

# Run Hono with Bun
bun run src/index.ts

# Or with Node
npx tsx src/index.ts

Common mistakes

Single-provider dependency: If OpenAI goes down, your pipeline stops. Add fallbacks to Anthropic or local models.

No human review path: For high-stakes content, add a manual approval step. n8n supports wait nodes for this.

Ignoring analytics: Track what performs. A/B test intros, CTAs, structures. The pipeline should learn.

Weak internal linking: Automate it. Manual linking doesn't scale and gets forgotten.

FAQ

How long does setup take?

A basic pipeline (trigger → draft → publish) takes a few hours. Adding research, validation, and linking adds another day or two.

Do I need RAG for this?

Not always. If your content relies on evergreen knowledge, prompt-based generation works fine. RAG helps when you need to reference internal docs or recent data.

What's the cost per article?

Depends on your LLM. With Claude or GPT-4, expect $0.10-0.50 per article for generation. Research and validation add marginal API costs.

Can this run serverless?

Yes. Hono is designed for edge and serverless runtimes. n8n cloud handles orchestration without self-hosting.

Wrapping up

An AI content pipeline isn't about replacing writers—it's about removing the friction around writing. n8n orchestrates the workflow, Hono handles the specialized logic, and you get consistent output without the manual grind.

Start simple: trigger, draft, publish. Add research and QA once that's stable. Then layer in internal linking and analytics. The compound effect is real—each improvement makes the next one easier.