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

European R&D is a €403 billion industry. In this industry, a single factual error in a grant reference is not just a typo. It is 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. The researcher trusts the date you cited. Later, the researcher finds that the date came from last year’s Work Programme instead of this year’s. The researcher just lost the chance at €14 million in funding. You 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 to 20 posts monthly with zero factual errors.

The system runs on two parallel workflows. One workflow covers high-accuracy factual content, where invented details destroy credibility. The other workflow covers technical how-to content, where outdated version numbers or wrong CLI flags break trust. Both workflows enforce verification, but the validation logic differs. The patterns transfer to any industry where “close enough” costs money. Examples include medical communications, legal content, financial analysis, and 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 is what you must assemble before you build the system.

Claude Desktop — Use the Pro or Team plan for Custom Connectors. The MCP integration runs through Claude Desktop, not the web interface. Download Claude Desktop from claude.ai and sign in 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 through the WordPress REST API. Filesystem MCP, built into Claude Desktop, handles local file operations for skill files and content libraries. Google Sheets MCP is optional and tracks batch operations and content calendars.

WordPress site — Use a self-hosted site with the REST API enabled and Application Passwords configured. WordPress.com sites work through the official WordPress.com connector. Self-hosted sites require the MCP adapter instead. 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 it runs a task. Skill files specify required inputs, gate sequences, validation rules, and security scan checklists. Store these files locally in a directory that Claude Desktop can reach through Filesystem MCP.

Verification sources — Use official documentation for your domain. For research communication, this means Work Programme PDFs, funder websites, and official grant portals. For technical content, this 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 publish in the 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 is not optional

Generic AI content workflows fail when a single error has financial consequences. The difference between “good enough” content and verified content is not visible until someone loses money by 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. The researcher misses the real deadline. €1.5 million in potential funding is gone. Your consultancy loses a client. Other researchers who saw that post now question every deadline you publish.

This is not hypothetical. European R&D operates at €403 billion annually. ERC grants alone distribute €16 billion across 2021 to 2027. Individual grants range from €150,000 (Proof of Concept) to €14 million (Synergy Grants with additional funding). These are not small stakes. A factual error in a grant reference post does not 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. The submission is rejected. A startup follows your tax strategy post and files using outdated thresholds. The startup faces penalties. A law firm cites a case summary from your legal blog, then finds that the cited precedent does not exist. This creates 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 policy changes that do not exist, all in a helpful, authoritative tone. The output reads well. It is just wrong.

Standard editorial workflows assume human writers check facts 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 correct itself. You must 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 allows zero 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 the WordPress save), and security scan (automated removal of internal references before publishing).

Workflow 2 handles MCP setup guides, automation workflows, and tool reviews. The accuracy requirement differs here. Readers expect working code, current version numbers, and accurate CLI flags. Outdated information breaks trust as much as invented facts do, 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 in different ways. 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 does not work, and the reader abandons 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 while it lets the validation logic adapt. The gates stay 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 it writes anything, the system searches official sources and presents a source inventory for approval. For an ERC grant post, this 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 progress until you approve the source list. This prevents the workflow from generating 2,000 words of well-structured prose that cites facts that do not exist.

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

Gate 3: SEO — Review keywords, internal links, and metadata before the 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. The workflow requires a minimum of two in-body links. Sidebar links come in addition. The SEO gate also validates excerpt length, meta description character count, and keyword placement.

Gate 4: Security scan — This gate removes personal references, internal project names, and real credentials before publishing, using an automated process. 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 recalled from memory, and intellectual property. This last category covers research ideas or proprietary insights from one client that must not appear in public content.

Each gate blocks forward progress until you approve it. The system does not draft body content until you approve the outline. It does not run SEO analysis until you approve the draft. It does not 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 does not rely on memory of what is in a post. It retrieves the current state before it makes 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. You do not need raw HTTP. 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 requires a minimum of two in-body links per post. Sidebar links come in addition. The search step prevents link rot, since the system does not 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 to 10 results. The system scores results by topic overlap, keyword match, and publication date, then selects the strongest two or three for in-body links.

Batch operation patterns

Once you validate the workflow on one post, you can authorize batch operations:

“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 in sequence: retrieve current content, identify sections that need updates, apply changes, verify against the source, save the 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 must go through the full approval gates. Once validated, the same logic applies to the remaining posts without re-approval at each gate. Final review happens after the batch completes. All posts stay 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, this 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 — Confirm the full section structure with technical accuracy. For an MCP setup guide, this might list: 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 — Validate keywords, internal links, and metadata. Technical posts target different keywords than factual posts do, for example “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 — Remove personal file paths, real credentials, internal project names, and 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. The same logic applies to API keys, spreadsheet IDs, WordPress credentials, and application passwords used in examples.

Research verification for technical accuracy

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

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

If you cannot verify a tool name, version number, or config key through official sources, the system flags it instead of inventing it. The draft includes a note: “Version number not confirmed. Verify against the current release before publishing.”

Code block standards

Every command, config snippet, or script gets its own fenced code block. Do not use inline code in prose. This makes copy and 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. Readers can copy and run commands without changing them.

The skill file architecture

Both workflows are defined as skill files, which are structured Markdown documents that Claude reads before it runs a task. 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 to 180 characters), Yoast SEO fields (focus keyphrase, meta description), sidebar link requirements

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

SEO validation rules — Primary keyword in the opening paragraph, primary keyword in at least one H2, description of 150 to 160 characters, title under 65 characters, keyword count of 5 to 8, accurate reading time, at least one external link, at least two internal links

The skill file acts as a contract. The workflow cannot proceed to the next step until the current gate’s requirements are met. This prevents a common failure mode: the AI generates plausible-sounding content that does not meet the 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 it starts a task. The skill defines what “done” looks like before the system generates any content.

Security scan: catching what should not publish

The security scan runs automatically before the WordPress save. It reads the full draft (frontmatter and body) and removes every internal or operational reference that must not appear publicly.

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

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

The scan does not strip public information. Pranoti Kshirsagar as author, named public institutions (CERN, Helmholtz, Fraunhofer), official tool names, and cited grant programmes remain. The scan targets unintentional leaks of private operational data.

Batch operations: scaling without breaking quality

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

The pattern: authorize the batch with full context, process posts in sequence, report changes, confirm before publishing.

Example batch authorization:

“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 stay in draft status for manual review before publishing. The batch operation assumes the workflow logic is correct. It does not assume the results are ready to publish.

When to use batch versus single-post workflow: use batch operations when the same change applies to multiple posts and the source is authoritative. Examples include deadline updates from an official Work Programme document and feature updates from product release notes. Batch operations do not work for content creation from scratch. Each new post requires individual research, outline approval, and SEO analysis.

What this workflow actually prevents

Here are real examples of errors that approval gates caught before publishing.

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

Outline gate — This gate caught a structural mismatch, where the outline included a section on “application fees” for a grant programme that charges no fees. Approving the structure before drafting prevented 500 words of invented policy details.

SEO gate — This gate identified a post with zero internal links, despite a site archive of over 200 related posts. The gate’s two-link minimum forced an internal linking strategy before the WordPress save.

Security scan — This gate removed 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, and caught file paths from local examples before they reached public view.

Intellectual property scan — This gate removed a research strategy detail from a grant guide. The detail 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 examples are not theoretical. Each one represents content that would have published without the gate structure. The workflow does not stop AI from generating errors. It stops 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 a compliance scan for HIPAA references, patient identifiers, and unapproved claims. The security scan should target patient data, institutional partnerships, and proprietary research.

Legal content — Use case law databases, statute text, and regulatory updates as verification sources. Approval gates catch citation errors, outdated precedents, and jurisdiction mismatches. The security scan must prevent client names, case details, and attorney work product from appearing in public content.

Financial analysis — Use SEC filings, earnings reports, and regulatory disclosures as sources. Gates verify data accuracy, calculation methods, and compliance with disclosure rules. The security scan removes client portfolio details, proprietary models, and internal research.

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

The pattern stays consistent: define verification sources for your domain, structure approval gates around your accuracy requirements, automate security scans for your sensitive data categories, and scale through batch operations once you validate the workflows.

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 here was European R&D. The principle applies wherever accuracy is not 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