What this automation does
Before any content goes live — blog posts, marketing emails, product descriptions, reports, or client deliverables — this automation runs it through an AI quality check. The AI scans for grammar and spelling errors, factual inconsistencies, brand voice deviations, missing sections, broken formatting, and compliance issues. It returns a pass/fail verdict with specific issues to fix.
Quality control is often the bottleneck in content workflows. Manual review is slow and inconsistent — different reviewers catch different things. AI provides a consistent quality baseline, catching the 90% of issues that are mechanical (typos, formatting, missing elements) so human reviewers can focus on strategic and creative feedback.
Tools you need
- Google Docs, Notion, or CMS: Where content is created and stored before publishing
- OpenAI API or Claude: Performs the multi-dimensional quality review ($0.05-0.15 per document)
- Make or Zapier: Triggers the QA check when content status changes to 'Ready for Review'
How to set it up
Step 1: Define your quality checklist. List everything that should be checked before content goes live: spelling and grammar, brand voice consistency (formal vs. casual, first-person vs. third-person), required sections (every blog post needs a meta description, every email needs a CTA), factual claims that need verification, and any compliance requirements (disclaimers, disclosures).
Step 2: Create a Make scenario triggered when content moves to 'Review' status in your CMS or project management tool. Extract the full content and send it to OpenAI or Claude with your quality checklist and brand style guide as context.
Step 3: Ask the AI to evaluate the content against each checklist item and return: an overall quality score (1-100), a pass/fail for each checklist item, specific errors with line references and suggested fixes, and a summary of the top 3 issues to address. Format the output as a structured review that is easy for the content creator to action.
Step 4: Post the QA results as a comment on the content document or send them to the creator via Slack. If the quality score is above your threshold (e.g., 85), mark it as ready for final human review. If below, send it back to the creator with the specific fixes needed. Track quality scores over time to identify recurring issues and provide targeted feedback to your team.
Cost breakdown
| Item | Cost | Notes |
|---|---|---|
| OpenAI API or Claude | $10-$20/mo | ~$0.10 per review at 100-200 documents/mo |
| Make or Zapier | $10-$15/mo | Based on review volume |
| Style guide setup | 1-2 hrs | One-time — document your brand guidelines for the AI |
| Setup time | 30-45 min | One-time |
| Total monthly | $20-$35/mo | Catches 95% of errors before publishing |
Frequently asked questions
AI is excellent at checking for brand voice consistency, structural completeness, and logical flow — not just spelling errors. When you provide clear guidelines (your style guide, required sections, tone examples), the AI applies them consistently across every piece of content. It cannot judge creative excellence, but it reliably catches mechanical and structural issues.
No. This handles the mechanical QA layer — the checks that should happen every time but often get skipped under deadline pressure. Human editors still provide strategic, creative, and nuanced feedback that AI cannot replicate. Think of it as a thorough first pass that lets your editor focus on higher-level improvements instead of catching typos.