Before deciding what to publish, optimize, automate, consolidate, or retire, you need a clear picture of what already exists and how it performs.
That was my focus this week. I created the first version of a repeatable system for building content inventories and auditing SEO and AI visibility.
I used two test websites, A and B, as practice environments. The goal wasn’t to make strategic recommendations yet. It was to build the layer that would allow me to provide more useful recommendations later.
Why This Matters
Without a clear view of your existing library, it’s difficult to manage the site, and new content only makes it harder.
Your team ends up with more URLs to maintain, more outdated assets, and no way to identify content gaps or pages competing for similar intent.
That’s why a content inventory matters. It gives you a way to see what exists, how it performs, what supports the customer journey, and what needs to be updated, consolidated, retired, or created next.
This is especially relevant for Web3, crypto, and fintech brands, where the customer journey is often long, fragmented, and trust-sensitive.
Content may be spread across blog posts, product pages, docs, tutorials, support pages, landing pages, and educational hubs. Some of it may get traffic but fail to support conversions. Some may be technically accessible but poorly connected to the customer journey. Some may be outdated, duplicated, or no longer aligned with what your brand wants to be known for.
For a Web3 infrastructure company, for example, weak tracking can make it harder to understand whether docs and SDK guides are actually supporting developer adoption. Similarly, for a wallet, exchange, or dApp, it can make it difficult to see whether support pages are answering the questions that block users from taking action.
Before deciding what to fix first, you need a clear view of the system you already have.
What I Worked On
This week, I started by confirming that GSC and GA4 tracking were in place for test websites A and B and resubmitted sitemaps, checked robots.txt, reviewed indexation, and fixed or submitted a few URLs where needed.
Then, I built a website auditing and reporting workspace for my own projects and future client work.
The core master spreadsheet, specifically, includes the following tabs:
- Website overview
- URL inventory
- GSC performance
- GA4 performance
- Technical issues
- AI visibility tests
- Notes
More important than the tabs themselves are the strategic questions they allow you to answer. For example:
- What content already exists?
- What is visible in search?
- What is not indexed?
- What is getting impressions but few clicks?
- What has technical issues?
- And, ultimately: What should be updated, consolidated, retired, or created next?
I also ran a very basic AI visibility check for website A.
I kept it simple; the goal was to see how AI might interpret the site’s positioning, topical signals, and visibility gaps based on a structured prompt sequence.
This was not an AI visibility audit. It simply helped clarify what I want to test more systematically later.
For me, AI visibility should not sit separately from SEO and content audits. It sits at the same table.
If a team wants to understand how visible it is in search, it needs more than rankings. It needs to understand how its content, positioning, authority signals, and topic associations are interpreted by AI retrieval systems.
I’ll work on this part more in-depth in the upcoming weeks.
Lastly, this week, I started working on workflow design.
I built an AI prompt library and two prompt chains — one for turning rough weekly notes into public progress logs (this very post you’re reading) and another for repurposing this content for LinkedIn.
The main goal is to make repeatable work faster and easier without lowering quality. The prompt chains still need work (the outputs required extensive editing), but that was part of the exercise. While AI can speed up the process, it can also create friction, and human review is always necessary.
This is increasingly relevant for content teams experimenting with AI.
A lot of companies are already using AI across research, drafting, optimization, repurposing, and QA workflows. But in practice, those systems are often fragmented with scattered prompts, inconsistent outputs, weak documentation, and no reliable review process.
Workflow design helps overcome this. I’m working on building AI-assisted processes that are structured and documented so it’s easy to scale and improve over time, and that support quality instead of undermining it.
What I’m Taking Forward
This week reinforced a simple principle that every content team should apply: Diagnose first, then prioritize.
- For SEO and content audits, that means understanding the existing content library and system before recommending new work.
- For AI visibility audits, it means checking how a site or brand is interpreted by AI models before making assumptions about discoverability.
- For content strategy roadmaps, it means using evidence to decide what to publish, optimize, consolidate, or retire.
- For workflow design, it means turning repeatable tasks into documented systems instead of relying on memory.
The baseline I built this week is still early. But it gives me a foundation to build from. Next, I want to refine the tracking tables and improve the AI visibility testing process.
The main takeaway this week is that better recommendations start with better visibility into the system.
