# AI-Assisted Content Gap Analysis

> Mining developer feedback across public and internal channels to quantify documentation gaps and ground the solution-guide content type in real evidence.

Rendered version: https://codyanthony.dev/case-studies/cloudflare/ai-content-gap-analysis/
Author: Cody Anthony (https://codyanthony.dev/about/)

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Cloudflare needed a new kind of documentation for customer goals that crossed product boundaries: a goal-first guide that starts from a customer's outcome and walks a complete workflow across whatever Cloudflare products it takes. This case study covers the research I ran to shape that content type and identify where the first guides should start. The work led to the [Solution Guide Content Type and Authoring System](/case-studies/cloudflare/solution-guide-system/).

## The challenge

To build the right guide, I first had to understand the gap it would fill, and that gap was not yet well understood. Cloudflare documentation was organized by product, so there was no clear view of where customers got stuck once their goals crossed products.

The unknowns were specific: which products customers were trying to combine, what outcomes they were pursuing, where the existing product docs left them stranded, and how common each gap was. Without that picture, the scope of the content type and the choice of where to start would come down to guesswork.

## Mining the signal

I used an internal AI research tool to analyze developer feedback from public community channels and internal feedback sources. The tool could find patterns, group related feedback, and synthesize trends through a chat interface.

I set a specific objective: surface feedback showing cross-product gaps that caused real user friction, then group and synthesize the themes. I directed the analysis, decided what counted as a genuine documentation gap, and turned the synthesized results into a content gap analysis report for stakeholders and documentation leadership.

The tool accelerated the research pass by grouping scattered feedback into visible themes. I then used those themes to define the documentation gap, identify the strongest starting point, and translate the findings into a content model and initial roadmap.

## What this demonstrates

- Data-driven content strategy grounded in real user-feedback evidence
- AI-assisted research with a human-defined objective and human synthesis judgment
- Turning scattered community signal into prioritized, decision-ready evidence
- Using research to shape both a content type and its initial roadmap
- A repeatable approach to community-feedback triage, rather than a one-off manual pass

## Outcomes

The analysis validated the need for a cross-product, goal-first content type and identified the highest-leverage place to start: the Application Security product space, where cross-product friction appeared most often and the largest customer audience stood to benefit.

Those findings shaped the first solution guides, including their scope, product combinations, and goal-first structure. The analysis became the evidence base for the content type and the authoring system built to produce it. The guides that came out of that work are collected in [Cloudflare Cross-Product Solution Guides](/case-studies/cloudflare/solution-guides/).