The Challenge

When I came on board, the content operation was in its early stages.

There was no systematic editorial workflow, no standardized content formats, no quality control framework, and no strategy for how content should be structured to perform in both traditional search and AI-powered discovery.

The blog needed to go from publishing content to building a content engine - one that could produce high volumes of SEO-optimized articles consistently, accurately, and at a level of quality that would hold up against competitors in one of the most crowded niches on the internet.

My Role & Approach

Editorial Systems & Workflow

I built the full content production pipeline from the ground up. Every article follows a structured process: web research and fact-verification first, then a full draft, then iterative revision against a custom editorial scoring system.

The scoring system checks for EEAT signals, keyword placement, meta description quality, and human writing quality, not just whether the article hits a word count. This workflow made quality repeatable, not dependent on any single writer's instincts.

AEO Methodology

I developed a documented Answer Engine Optimization methodology - a structured approach to engineering content that AI search engines retrieve and cite.

This goes beyond traditional SEO. It's about understanding how large language models extract, evaluate, and surface information, and then structuring content so it becomes the answer these systems reach for.

The methodology covers answer-first architecture, machine-readable structure, and post-publish citation auditing across ChatGPT, Perplexity, Claude, and Copilot Search.

[If you have your Authory AEO results link, add it here: "See my AEO citation results →"]

Content Formats & Standards

I standardized multiple content formats, each designed for a specific purpose and audience intent. Tool reviews follow a consistent structure — TL;DR, pricing, Best For sections — so readers can find what they need fast and Google can parse the content clearly. AI news rewrites use journalism-style openings with varied structure, fewer subheadings, and inline hyperlinks instead of bracketed citations. Category roundups are built for comparison queries.

Across all formats, the writing standard is conversational, clear, and pitched at a sixth-grade reading level — not because the audience is unsophisticated, but because accessible writing performs better in both traditional and AI-powered search.

Quality Control & Fact-Checking

In AI content, getting the facts wrong isn't just embarrassing. It destroys trust with an audience that knows the space.

I implemented rigorous fact-checking workflows, with a particular focus on pricing verification against official product pages. Third-party sources get it wrong constantly, and we caught it constantly.

A few examples: we identified and corrected Devin AI's pricing overhaul - they'd moved from a flat $500/month to a tiered Free/Pro/Max/Teams structure that most competing articles still had wrong. We caught Civitai's Buzz system changes that followed their May 2025 payment processor shift, which virtually no other publication had updated. And these aren't edge cases. This is what happens when you actually verify instead of copying from the last person who wrote about it.

AI News Coverage

Beyond evergreen SEO content, I led the production of rapid-turnaround AI news rewrites covering developments from OpenAI, Anthropic, Google DeepMind, DeepSeek, xAI, and others. Each piece came with a full extras package - SEO keyword, URL slug, meta description, image prompt, and five headline options. We produced roughly 30 of these in a single month during a particularly active news cycle, each written with journalism-style openings, interesting subtitles, and a tone that reads like analysis, not aggregation.

I help brands grow organic visibility through SEO, GEO, and AEO-optimized content - from strategy to execution

ONOME AKPODONOR