The Three Pillars of an AI Knowledge Base
Separating storage, structure, and access for better control
I‘ve spent years switching AI note apps, like Mem, Notion, Roam, Anytype, and each one failed in a different way. That’s because a knowledge base is actually doing three separate jobs: storage, structure, and access. Almost no app treats them as three.
Have you ever bought an app that promised to finally get you organized, used it faithfully for a few months, and then quietly let it slide back into the pile of apps you meant to use?
I’ve done this more times than I want to admit, and each time I told myself the problem was the tool.
It never was.
As exciting as these new apps are, no piece of software does everything you need it to do, perfectly, forever.
Something is always missing, or something almost works but not quite, and eventually you have to connect it to something else, or build the missing piece yourself.
This is exactly why workflows and stacks matter so much right now, for creators and for content professionals both.
Though many will claim otherwise, nobody’s shipping the one tool that does it all. That’s why you’ll often see people build their own combinations instead.
For example, you’ve found the perfect notes app, but handle your tasks somewhere else. Then to get AI to work with both you need to link it somehow. This is why you see so many “extensions” and “integrations” in spaces like Claude and ChatGPT.
Creating “app stacks” that match your workflow like this is a skill in and of itself that is becoming a job in many organizations.
I know tech writers and content designers who make an entire living doing exactly this for organizations, and it’s one of the things that fascinates me most about that world.
There’s no perfect app or perfect stack sitting out there waiting to be discovered, because choosing and connecting tools always depends on who’s actually going to use what you make, how it needs to move, and what it has to become later.
What most people don’t realize is that building these workflows is rhetorical, because workflows shape the spaces where we make meaning.
There’s no perfect app or perfect stack sitting out there waiting to be discovered, because choosing and connecting tools always depends on who’s actually going to use what you make, how it needs to move, and what it has to become later.
In the end, the environment you build shapes what you’re able to think, notice, and make inside it, long before AI ever enters the picture.
What AI does is raise the price of getting that wrong. A bad stack used to just cost you time and money locally. Now with AI it costs you at scale.
I’ve been doing a version of this for my own knowledge base for a few years now, exploring various approaches to personal knowledge management, or PKM — a term you may not have heard, but is attached to a category of apps you’ve almost certainly seen. Notion, Roam, Obsidian, Evernote, even a shoebox of index cards if you want to go back far enough are all PKM built to help you capture, organize, and retrieve what you know.
Three invisible layers to modern knowledge apps
I’ve spent an embarrassing number of hours exploring personal knowledge management, because no single one of these apps has ever done everything I actually need.
The one I’ve been watching the longest, for about four years now, is Anytype — a newer PKM tool most people outside this world have never heard of.
Anytype treats your knowledge as objects instead of notes in a folder — things with real relationships to each other, private by default, but easy to open up the moment you actually want to share or collaborate.
My students use it for their writing portfolios because they can build the structure themselves, and they decide what becomes public.
What it still doesn’t really have is AI. It’s coming. There’s supposedly a working MCP integration now, though I haven’t managed to get it running cleanly on my end yet.
I actually respect how slowly and methodically the team has approached AI, but that can get frustrating at times.
But four years is a long wait, and it’s given me time to notice something else about Anytype. The same object-oriented control that makes it so good is also what makes it exhausting. Deciding what every single thing is, and how it relates to everything else, is real work.
Some weeks I don’t have it in me.
That’s probably the actual reason I haven’t moved my whole knowledge base over yet, more than any missing feature. The AI I’ve been waiting for is exactly the thing that would make that easier. It just isn’t here.
There’s a decent chance I move into Anytype fully once it is. The version of my knowledge base I’ve been building is portable enough that I could hand it to Anytype, or whatever comes after Anytype, without starting over.
But after thinking about it, I realized that this disappointment wasn’t random or even about a single tool.
An AI knowledge base is doing three separate jobs, and almost no product on the market treats them as three or does all three right.
There’s storage or where your knowledge actually lives, as files you control, not rows in someone else’s database you’d need an export tool to escape.
There’s structure or how you organize, browse, and edit it. Different apps have different approaches that determine what you can do. This is the pillar Anytype nails — and also the one that wears me out.
And there’s access or how AI actually reads what you’ve built. It should be swappable, since the model behind it is the piece most likely to change on somebody else’s schedule, not yours. This is the piece Anytype still doesn’t have.
Name the three jobs, and every tool I’ve tried turns out to be strong in one of these, weak in another, and often has no real answer for the third.
My graveyard of knowledge apps
Mem was the closest I came to AI integration, but only by sacrificing the other two..
I imported everything, and for several months I was genuinely impressed. It surfaced things I’d forgotten. Then an update shipped and it didn’t feel the same. I went looking for the knob that I could use to adjust how AI was working.
There wasn’t one.
The organizing scheme was determined by the developers, not me, and there was no way to change it. I ended up building projects in Claude instead, and stopped opening Mem.
I was also an early adopter of Notion, and put my entire life into it for awhile. Within a year and a half it had become a beast I didn’t want to open.
Not because the tool broke, but because the structure I’d built inside it had gotten so tangled that finding anything meant a lot of time and effort that I just kept avoiding.
Notion does storage, structure, and now its own AI layer, all under one roof, which sounds like the dream until you actually try to change just one of the layers.
I also tried Roam. For a while it felt like the answer to everything. I loved that there were no folders at all, just links, with the organization emerging from how ideas actually connected instead of where I’d decided to file them.
I loved it, at least for the first few months.
Then I noticed I was spending more time linking than thinking, more time maintaining the web than using it. There was always one more connection you could add, with the app itself having no opinion about when you’d done enough.
So far the tool that gets it right the most is Twos. I still use it, especially when I’m not sure where to put something.
The structure pillar here is deliberately thin. Twos never asked me to decide much, so I never built anything that could get tangled the way Notion did. You’ve got “lists” and you’ve got “things”. That’s it.
It has AI now and MCP connections that work well, but not many of the organizational “knobs” I might want in a complete knowledge base that I use with AI.
What’s missing in most knowledge apps
But all five tools turn out to be missing the one thing I’ve discovered to be key in developing knowledge bases for AI. Separation of these three layers for better control.
If the AI reading your files is a distinct layer from the files themselves, it can help maintain the structure instead of either ignoring it or owning it outright.
It can catch that two tags mean the same thing.
It can flag a folder that’s drifted from its own rule.
It can follow your instructions on how you want the knowledge base to work.
AI can do the unglamorous upkeep that turned my Notion into a beast and my Roam into a chore.
That’s work I always meant to get around to and never did, and doing it doesn’t require deciding for me what the structure should be in the first place.
That only works because the layer doing that work isn’t the same layer holding the knowledge.
Mem couldn’t offer me this, because it never separated the two to begin with. The AI wasn’t helping me maintain my structure. It was replacing my judgment about what the structure should be.
Anytype could offer me this eventually, once its AI layer actually works, precisely because the object model and the AI are being built as two separate pieces, not one piece trying to do both jobs. That’s the whole reason I’m still waiting instead of leaving.
But in the meantime, I’m building my own personal knowledge base one layer at a time.
Markdown files on my hard drive.
Obsidian as my “human interface” for drafting and editing.
Claude Cowork for maintaining and using the knowledge base.
And I can use markdown documents and skills in the knowledge base to keep guardrails on the AI and help me test and evaluate how the knowledge is working.
More explorations to come
This is the first of several pieces I’m planning to write on this exact problem, working through each pillar in more depth and exploring various structured approaches to building and maintaining an AI-ready personal knowledge base.
I’m already exploring how skills and information types make this workflow work better, as well as different ways of connecting AI to a markdown knowledge base.
Next, I’m pulling one of those skill files apart, line by line, to show what happens when nobody decides what belongs in it.
If you want to see some of that work, I’ve been building a public library of the skills I use for my own writing and content workflows — real, working files, organized by the same five information types I keep coming back to, available to paid subscribers to copy and adapt for their own setup.
It’s a live library, not a one-time download, so it keeps changing as my own practice does.
So ... of the three jobs your knowledge base is doing right now, which one have you never actually chosen? Where it lives, how you’ve structured it, or how AI gets to it?
Leave a comment. I’d genuinely like to know which failure modes you’re living with.