CASE STUDY · DIGITAL PUBLISHING
AI Content & SEO System for Multi-Vertical Publisher
The Challenge
A digital publisher running 5 niche content verticals needed to scale from 10 articles per week to 50+ without proportionally growing their editorial team. They had tried generic AI writing tools, but the output was too shallow for their audience — their verticals relied on topical authority, and low-quality content was actively hurting their search rankings. They needed a system that could produce long-form, research-backed articles with consistent quality, while keeping their editors in the loop for final approval.
The Solution
- Custom WordPress plugin for AI article generation with configurable editorial rules per vertical — tone, depth, target word count, required sections, and forbidden phrases are all set per category.
- Multi-pass generation pipeline: topic research → outline generation → section-by-section writing → fact-checking pass → SEO optimization. Each step has quality gates that reject and regenerate below-threshold outputs.
- Scheduled publishing queue with WordPress cron automation. Articles generated overnight, queued for human review each morning. Editors get a dashboard with one-click approve, reject (with feedback), or edit workflow.
- Built-in SEO layer: keyword density targeting, automatic internal linking to related existing posts, meta description generation, and Schema.org Article markup injected on publish.
The Result
The editorial team went from spending 80% of their time writing to spending 80% on strategy, quality checks, and audience research. The rejection feedback loop improved generation quality week over week — first-pass approval rate climbed from 68% in week 1 to 91% by week 6. Three of the five verticals hit new all-time traffic records within the first quarter.
Tech Stack
What was unique
The multi-pass architecture was critical. A single-prompt approach produced articles that read well superficially but lacked the depth and internal consistency the publisher needed. Our pipeline generates an outline first, then writes each section individually with context from the previous sections, then runs a coherence pass across the full article. The feedback loop is the real differentiator: when an editor rejects an article with notes like “too surface-level on pricing comparison” or “missing competitor analysis,” those notes feed back into the prompt templates for that vertical. The system gets smarter with use. After 6 weeks, the rejection rate had dropped from 32% to under 9% without any manual prompt engineering.
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