Build an AI-Powered Content Workflow for a Billion-Euro Industry

In European R&D — a €403 billion industry — a single factual error in a grant reference isn’t just a typo. It’s a million-euro mistake for the researcher who trusted your content. Here is how I built the workflow to prevent it.

European R&D spending reached €403.1 billion in 2024. Individual grants range from €150,000 to €14 million. A researcher reads your blog post about an ERC deadline, trusts the date you cited, and discovers too late it came from last year’s Work Programme instead of this year’s. They’ve just lost their shot at €14 million in funding. You’ve lost your reputation.

This is the content workflow built to make that impossible. Approval gates block publishing until facts are verified. Automated security scans catch leaked client details and intellectual property before content goes live. MCP integration handles batch operations across 15–20 posts monthly with zero factual errors.

The system runs on two parallel workflows — one for high-accuracy factual content where invented details destroy credibility, and one for technical how-to content where outdated version numbers or wrong CLI flags break trust. Both enforce verification, but the validation logic differs. The patterns transfer to any industry where “close enough” costs money — medical communications, legal content, financial analysis, compliance reporting.

What you need before starting

This workflow requires Claude Desktop with MCP integration, a self-hosted WordPress site, and a structured prompt architecture. Here’s what you’ll need assembled before building the system.

Claude Desktop — Pro or Team plan for Custom Connectors. The MCP integration runs through Claude Desktop, not the web interface. Download from claude.ai and authenticate with your account.

MCP servers — Three connectors handle different parts of the workflow. The WordPress MCP connector (claudeus-wp-mcp) manages post creation, metadata updates, and content retrieval via WordPress REST API. Filesystem MCP (built into Claude Desktop) handles local file operations for skill files and content libraries. Google Sheets MCP (optional) tracks batch operations and content calendars.

WordPress site — Self-hosted with REST API enabled and Application Passwords configured. WordPress.com sites work through the official WordPress.com connector, but self-hosted sites require the MCP adapter. Your site must support the REST API endpoints for posts, taxonomies, and metadata.

Skill file architecture — A structured prompt library defines approval gates, output formats, and security scans for each content type. Skill files are Markdown documents that Claude reads before executing tasks. They specify required inputs, gate sequences, validation rules, and security scan checklists. Store these locally in a directory accessible to Claude Desktop via Filesystem MCP.

Verification sources — Official documentation for your domain. For research communication, that means Work Programme PDFs, funder websites, and official grant portals. For technical content, that means product release notes, API specifications, and official documentation sites. The workflow rejects invented facts — every claim must trace to a verifiable source.

If you’re publishing in medical, legal, or financial domains, add compliance documentation and regulatory guidance to your verification source list. The approval gates adapt to your industry’s accuracy requirements.

The problem: When accuracy isn’t optional

Generic AI content workflows fail when a single error has financial consequences. The difference between “good enough” content and verified content isn’t visible until someone loses money trusting the wrong information.

A researcher applies for an ERC Starting Grant using the deadline listed in your blog post. The deadline is wrong — you cited last year’s Work Programme instead of this year’s. They miss the real deadline. €1.5 million in potential funding, gone. Your consultancy loses a client. Other researchers who saw that post now question every deadline you’ve published.

This isn’t hypothetical. European R&D operates at €403 billion annually. ERC grants alone distribute €16 billion across 2021–2027. Individual grants range from €150,000 (Proof of Concept) to €14 million (Synergy Grants with additional funding). These aren’t small stakes. A factual error in a grant reference post doesn’t just damage your reputation — it costs someone real money.

The same logic applies across regulated industries. A medical device company trusts your compliance guide and submits documentation using the wrong FDA classification. Rejected. A startup follows your tax strategy post and files using outdated thresholds. Penalties. A law firm cites a case summary from your legal blog, only to discover the precedent cited doesn’t exist. Malpractice risk.

AI-generated content compounds the problem. Language models produce fluent, confident-sounding text regardless of factual accuracy. Without verification gates, the system will invent grant amounts, fabricate deadlines, and cite non-existent policy changes — all while maintaining a helpful, authoritative tone. The output reads well. It’s just wrong.

Standard editorial workflows assume human writers fact-check as they write. AI workflows require explicit verification gates because the model has no concept of “true” versus “plausible-sounding.” You cannot rely on the AI to self-correct. You build a system that blocks forward progress until facts are verified against primary sources.

The dual-track AI content workflow system

High-accuracy factual content and technical how-to content require different validation logic. Both need verification, but the failure modes differ.

Workflow 1 handles grant guides, funding calls, and compliance summaries. The accuracy requirement is zero tolerance for invented details. Every claim about a deadline, funding amount, eligibility criterion, or submission requirement must trace to an official source. The workflow enforces four approval gates: research (source inventory approved before writing), outline (section structure confirmed before drafting), SEO (keywords and links reviewed before WordPress save), and security scan (automated strip of internal references before publishing).

Workflow 2 handles MCP setup guides, automation workflows, and tool reviews. The accuracy requirement is different — readers expect working code, current version numbers, and accurate CLI flags. Outdated information breaks trust just as much as invented facts, but the verification process targets technical specifications rather than policy documents. The workflow enforces the same four gates with adjusted validation criteria.

The distinction matters because the two content types fail differently. A factual error in a grant guide means someone misses a deadline or submits to the wrong programme. A technical error in an MCP setup guide means someone’s configuration doesn’t work and they abandon the tutorial. Both damage credibility, but the research process differs.

For grant content, verification means retrieving Work Programme PDFs, checking official funder websites, and cross-referencing multiple years to confirm changes. For technical content, verification means searching product release notes, checking GitHub repos for current version numbers, and testing configuration snippets against official documentation.

Running both workflows through the same approval gate structure keeps the system consistent whilst allowing validation logic to adapt. The gates remain the same (research, outline, SEO, security scan). The validation criteria change based on content type.

Workflow 1: High-accuracy factual posts

Grant guides and funding call summaries follow a four-gate structure designed to catch errors before they compound.

Gate 1: Research — Before writing anything, the system searches official sources and presents a source inventory for approval. For an ERC grant post, that means retrieving the current year’s Work Programme PDF, checking the ERC website for deadline confirmations, and noting any changes from the previous year’s call. The research gate blocks until the source list is approved. This prevents the workflow from generating 2,000 words of well-structured prose citing facts that don’t exist.

Gate 2: Outline — Section structure and information hierarchy confirmed before drafting body content. The outline specifies which facts go in which sections without writing the prose yet. For a grant post, that might be: eligibility criteria, funding amounts, submission deadlines, evaluation process, success rates. Approving the outline at this stage prevents structural rework later.

Gate 3: SEO — Keywords, internal links, and metadata reviewed before WordPress save. The system runs keyword-based searches across the site’s post archive to find relevant internal links. For a grant post about ERC Starting Grants, the search might return posts about proposal writing, Horizon Europe, or other ERC schemes. Minimum two in-body links required; sidebar links are additional. The SEO gate also validates excerpt length, meta description character count, and keyword placement.

Gate 4: Security scan — Automated strip of personal references, internal project names, and real credentials before publishing. The scan catches file paths used as examples (/Users/pranotikshirsagar/... becomes ~/path/to/your/file), client names not cited publicly, real email addresses, internal tool names, API keys, WordPress post IDs from memory, and intellectual property — research ideas or proprietary insights from one client that shouldn’t appear in public content.

Each gate blocks forward progress until approved. The system doesn’t draft body content until the outline is approved. It doesn’t run SEO analysis until the draft is approved. It doesn’t save to WordPress until the security scan passes.

MCP integration for WordPress operations

The claudeus-wp-mcp connector handles WordPress operations through function calls that translate to REST API requests.

Retrieving an existing post for edits:

claudeus_wp_content__get_posts(
  filters: {
    'include': [12345],
    'status': 'any'
  }
)

This returns the full post object including title, content, excerpt, metadata, and custom fields. The system doesn’t rely on memory of what’s in a post — it retrieves current state before making changes.

Saving a draft after approval:

claudeus_wp_content__update_post(
  id: 12345,
  title: "ERC Starting Grant 2027: Everything Researchers Need to Know",
  content: APPROVED_BODY_HTML,
  status: "draft",
  excerpt: APPROVED_EXCERPT,
  meta: {
    '_yoast_wpseo_focuskw': 'ERC Starting Grant 2027',
    'cybocfi_hide_featured_image': 'yes'
  }
)

Application Passwords authenticate the connection. The MCP server translates these function calls into WordPress REST API requests — no raw HTTP required. Configuration lives in claude_desktop_config.json with the site URL, username, and Application Password stored as environment variables.

Internal linking strategy

After drafting, keyword-based searches run across the site’s post archive to find relevant internal links:

claudeus_wp_search__search(
  query: "proposal writing",
  type: "post"
)

This returns post titles and URLs. The workflow specifies minimum two in-body links per post; sidebar links are additional. The search step prevents link rot — the system doesn’t rely on memory of what posts exist. It searches, evaluates relevance, and inserts links based on current content.

For a post about ERC grants, relevant searches might include: “proposal communication”, “Horizon Europe”, “research funding”, “grant application”. Each search returns 5–10 results. The system scores by topic overlap, keyword match, and publication date, then selects the strongest 2–3 for in-body links.

Batch operation patterns

Once the workflow is validated on one post, batch operations can be authorised:

“Apply the same ERC 2027 Work Programme updates to posts 12345, 12346, 12347, and 12348. Proceed without confirmation between posts.”

The system processes each post sequentially: retrieve current content, identify sections requiring updates, apply changes, verify against source, save draft, report completion. This approach scaled ERC deadline updates across four grant posts (Starting, Consolidator, Advanced, Synergy) in one session.

Batch logic requires confidence that the workflow is correct. The first post goes through full approval gates. Once validated, the same logic applies to remaining posts without re-approval at each gate. Final review happens after the batch completes — all posts in draft status for manual confirmation before publishing.

Workflow 2: Technical how-to content

MCP setup guides, automation workflows, and tool reviews follow the same four-gate structure with adjusted validation criteria.

Gate 1: Research — Search official documentation and present verified technical details before writing. For an MCP integration guide, that means checking Anthropic’s documentation for current Claude Desktop version requirements, searching GitHub repos for MCP server installation commands, verifying configuration file locations, and confirming API parameter names. The research gate catches outdated information before it enters the draft.

Gate 2: Outline — Full section structure with technical accuracy confirmed. For an MCP setup guide, that might be: prerequisites and version requirements, installation steps, configuration file setup, authentication, testing the connection, troubleshooting common errors. Each section specifies which commands, config snippets, or API calls will appear without writing the full prose yet.

Gate 3: SEO — Keywords, internal links, metadata validated. Technical posts target different keywords than factual posts — “Claude Desktop MCP setup” instead of “ERC grant deadline”. The internal linking strategy searches for related technical content rather than grant guides.

Gate 4: Security scan — Strip personal file paths, real credentials, internal project names, site-specific details. Early drafts of MCP guides included real local file paths as examples (/Users/pranotikshirsagar/Documents/CLAUDE/...). The scan catches these and replaces them with generic placeholders (~/path/to/your/config/) before the draft reaches review. Same logic for API keys, spreadsheet IDs, WordPress credentials, and application passwords used in examples.

Research verification for technical accuracy

Technical posts verify against product documentation, GitHub repos, and release notes. For an MCP WordPress guide:

• Claude Desktop version requirements — search Anthropic docs for minimum version, platform compatibility, installation URLs
• MCP server installation commands — verify exact npx syntax, package names, version pinning
• Configuration file locations — confirm paths for macOS (~/Library/Application Support/Claude/) vs Windows (%APPDATA%\Claude\)
• REST API endpoints — check WordPress documentation for endpoint structure, required parameters, authentication methods
• Feature availability — verify which features exist in which pricing tiers (Pro vs Team vs Enterprise)

If a tool name, version number, or config key can’t be verified through official sources, the system flags it rather than inventing. The draft includes a note: “Version number not confirmed — verify against current release before publishing.”

Code block standards

Every command, config snippet, or script gets its own fenced code block. No inline code in prose. This makes copy-paste reliable for readers.

Configuration example:

{
  "mcpServers": {
    "claudeus-wp-mcp": {
      "command": "npx",
      "args": ["-y", "claudeus-wp-mcp"],
      "env": {
        "WP_SITES_PATH": "/path/to/your/wp-sites.json"
      }
    }
  }
}

WordPress REST API call example:

curl -X POST https://yoursite.com/wp-json/wp/v2/posts \
  -u "username:application-password" \
  -H "Content-Type: application/json" \
  -d '{"title":"Test Post","content":"Content here","status":"draft"}'

Each code block includes the language identifier for syntax highlighting. Paths use generic placeholders rather than real directory structures. Commands are copy-pastable without modification.

The skill file architecture

Both workflows are defined as skill files — structured Markdown documents that Claude reads before executing tasks. Each skill specifies required inputs, approval gates, output formats, security scans, and SEO rules.

A skill file for grant posts includes:

Required inputs — Topic, target reader, key takeaway, tools or programmes covered, specific facts to verify

Approval gate sequence — Gate 1 (research), Gate 2 (outline), Gate 3 (SEO), Gate 4 (security scan) with blocking logic between gates

Output format — Hugo frontmatter structure, heading hierarchy, excerpt length (140–180 chars), Yoast SEO fields (focus keyphrase, meta description), sidebar link requirements

Security scan checklist — Internal file paths, client names, personal locations as private context, real email addresses, internal project names, real credentials or IDs, WordPress post IDs from memory, intellectual property (research ideas, proprietary client insights)

SEO validation rules — Primary keyword in opening paragraph, primary keyword in at least one H2, description 150–160 chars, title under 65 chars, keywords count 5–8, reading time accuracy, external links minimum 1, internal links minimum 2

The skill file acts as a contract. The workflow cannot proceed to the next step until the current gate’s requirements are satisfied. This prevents the common failure mode where AI generates plausible-sounding content that doesn’t meet actual specifications.

Skill files live in a local directory accessible via Filesystem MCP:

~/Documents/CLAUDE/SKILLS/
  grant-post/
    SKILL.md
  ai-integration-post/
    SKILL.md
  linkedin-funding-post/
    SKILL.md

Claude reads the appropriate skill file before starting a task. The skill defines what “done” looks like before any content is generated.

Security scan: Catching what shouldn’t publish

The security scan runs automatically before WordPress save. It reads the full draft (frontmatter and body) and strips every internal or operational reference that shouldn’t appear publicly.

The intellectual property category is critical for consultancies serving multiple clients in the same field. A grant consultant works with researchers across multiple institutions. An insight from one client’s proposal strategy shouldn’t leak into a public blog post where competing researchers might see it. The scan flags and removes proprietary insights before publishing.

Scan procedure: read full draft, list every flagged instance before replacing anything, apply all replacements, output confirmation. The scan outputs either “Security scan complete — [N] references replaced” or “Security scan complete — nothing to replace.”

Public information isn’t stripped. Pranoti Kshirsagar as author, named public institutions (CERN, Helmholtz, Fraunhofer), official tool names, and cited grant programmes remain. The scan targets unintentional leakage of private operational data.

Batch operations: Scaling without breaking quality

Once a workflow is validated on a single post, batch operations apply the same logic across multiple posts without re-approval at each gate.

The pattern: authorise the batch with full context, process sequentially, report changes, confirm before publishing.

Example batch authorisation:

“The ERC 2027 Work Programme changed three deadlines across Starting, Consolidator, Advanced, and Synergy grants. Apply these updates to posts 12345, 12346, 12347, and 12348. Use the official Work Programme PDF as the source. Proceed without confirmation between posts.”

Sequential processing logic:

1. Retrieve current post content via claudeus_wp_content__get_posts
2. Identify sections containing deadline references
3. Compare current content against Work Programme source
4. Apply deadline updates with source citation
5. Verify changes against approval criteria
6. Save as draft via claudeus_wp_content__update_post
7. Report: “Post [ID] updated — [specific changes made]”
8. Move to next post

After the batch completes, all posts remain in draft status for manual review before publishing. The batch operation assumes the workflow logic is correct, not that the results are automatically publishable.

When to use batch vs single-post workflow: batch operations work when the same change applies to multiple posts and the source is authoritative. Deadline updates from an official Work Programme document. Feature updates from product release notes. Batch operations don’t work for content creation from scratch — each new post requires individual research, outline approval, and SEO analysis.

What this workflow actually prevents

Real examples of errors caught by approval gates before publishing:

Research gate — Prevented a grant post from citing funding amounts invented by the model. The post claimed ERC Starting Grants provided “up to €2 million” when the actual amount is €1.5 million. The research gate required source verification before drafting, caught the error at outline stage.

Outline gate — Caught structural mismatches where the outline included a section on “application fees” for a grant programme that doesn’t charge fees. Approving structure before drafting prevented 500 words of invented policy details.

SEO gate — Identified a post with zero internal links despite a site archive of 200+ related posts. The gate’s minimum-two-links requirement forced internal linking strategy before WordPress save.

Security scan — Stripped real WordPress post IDs used as examples ("/wp-admin/post.php?post=12345" became “/wp-admin/post.php?post=YOUR_POST_ID”), replaced real Application Passwords shown in config examples, removed mentions of specific client institutions in case study references, caught file paths from local examples before they reached public view.

Intellectual property scan — Removed a research strategy detail from a grant guide that came from a client’s confidential proposal review. The insight was accurate and helpful, but publishing it would have revealed a client’s competitive approach to other applicants in the same field.

These aren’t theoretical. Each example represents content that would have published without the gate structure. The workflow doesn’t prevent AI from generating errors — it prevents errors from reaching publication.

Transferable patterns for other industries

The approval gate structure adapts to any domain where factual errors have consequences.

Medical communications — Replace grant verification with clinical trial data, FDA guidance documents, and peer-reviewed publications. Add compliance scan for HIPAA references, patient identifiers, and unapproved claims. Security scan targets patient data, institutional partnerships, and proprietary research.

Legal content — Verification sources become case law databases, statute text, and regulatory updates. Approval gates catch citation errors, outdated precedents, and jurisdiction mismatches. Security scan prevents client names, case details, and attorney work product from appearing in public content.

Financial analysis — Sources shift to SEC filings, earnings reports, and regulatory disclosures. Gates verify data accuracy, calculation methods, and compliance with disclosure rules. Security scan removes client portfolio details, proprietary models, and internal research.

Compliance reporting — Verification against regulatory frameworks, audit requirements, and industry standards. Gates confirm policy interpretation, procedure accuracy, and deadline compliance. Security scan strips internal control details, audit findings, and organisational structure.

The pattern remains consistent: define verification sources for your domain, structure approval gates around your accuracy requirements, automate security scans for your sensitive data categories, scale through batch operations once workflows are validated.

Start with one content type. Build the skill file. Validate the workflow on a single piece of content. Then scale to batch operations and multiple content types. The €403 billion context was European R&D. The principle applies wherever accuracy isn’t optional.

Related reading on The Science Talk

• How to Connect Your Self-Hosted WordPress Site to Claude Desktop — Step-by-step guide to connecting WordPress via MCP for content workflow automation

Browse all AI integration guides at pranoti.thesciencetalk.com/ai-guides