You have spent years reading, studying, and accumulating expertise. Journal articles. Conference notes. Regulatory updates. Case studies. Board reports. Research papers.
Where is all of it right now?
If the answer is “scattered across folders, email, PDFs, and my memory” — you are not disorganized. You are dealing with a problem that has never had a good solution.
Every note-taking system, filing method, and knowledge management tool you have tried shares the same flaw: they need a librarian. Someone has to file new material, cross-reference it against what is already on the shelf, update old entries when new information arrives, and spot contradictions between sources. That work is tedious, time-consuming, and unrewarding in the moment — so nobody does it. The system fills up. The connections never get made. Eventually you stop using it.
This has been true for eighty years. In 1945, a scientist named Vannevar Bush imagined a personal knowledge machine that would build trails of connections between documents. He was right about the value. He could not solve the maintenance problem. Neither could Evernote, OneNote, Google Drive, or any of the systems you have tried since.
I build personal knowledge libraries where AI handles all the filing, organizing, and cross-referencing — the work you have never had time to do yourself.
Here is how it works. You have a private library on your computer with three parts:
Every article, paper, report, and note you want to keep. They sit on your hard drive, untouched. Nobody edits them, reorganizes them, or moves them to someone else's server.
For every item in the collection, the AI creates a summary, identifies the key people and ideas mentioned, and links it to everything related that is already in the library. When two sources disagree, the AI flags the contradiction. When a new article fills a gap identified by an earlier one, the AI records the connection. The catalog is a living, evolving document that gets richer every time you add something.
A set of rules I write specifically for your library — how to file material in your field, what to watch for, what to flag for your attention. The AI reads these rules every time it works on your library.
Your daily interaction with the system is two things: drop a file in, and ask questions. Everything in between — the filing, the summarizing, the cross-referencing, the contradiction detection — is handled.
This is the part that matters most.
The first article you add is worth a summary. The tenth article is worth a summary plus connections to nine others. The hundredth article resolves contradictions identified at article fifty and fills gaps surfaced at article seventy-five.
A filing cabinet gets bigger over time. This gets smarter. Every piece of material you add makes every existing piece more valuable, because the AI finds and records the connections between them. Questions you ask six months from now return better answers than questions you ask today — more material, more connections, richer context.
If you think in financial terms: this is compound interest on intellectual capital.
You read a new journal article on Tuesday. You save it to a folder on your computer. The AI reads it, writes a summary, updates every related entry in your library, flags a contradiction with something you read in January, and logs what changed. Total effort on your part: saving a file.
On Thursday, you are preparing for a meeting and need to recall what your sources say about a specific topic. You ask the question in plain English. The AI searches your organized catalog — not the internet, not a general database, but your personal, curated collection — and returns a cited answer drawn from multiple sources. You get the answer in seconds, with links back to the originals.
At the end of the year, your library contains everything you read, organized and cross-referenced. You have not re-read a single article to remember what it said. You have not searched through folders. You have not carried the mental load of remembering where things are. The knowledge you accumulated in January is still working for you in December — connected to everything that came after it.
The files, the organized catalog, everything — it lives on your hard drive, not on someone else's server. The only external interaction is when the AI processes a file or answers a question, which requires a brief connection to the AI service. Your documents are not stored there. Nothing leaves your possession.
The entire library is built on plain text files in an open format. No proprietary software traps your data. No company can change the format, raise the price, or shut down the service and take your knowledge with it. If you decided to stop using AI entirely, every file would still open in any text editor on any computer.
Your knowledge is never locked in. Every file in the library is plain text markdown — an open format any application can read. There is no proprietary database, no vendor-specific format, and no dependency on any single AI tool. If Anthropic disappeared tomorrow, your library would still exist on your hard drive, fully readable and intact. Any AI tool that reads text files can pick up where Claude left off. The value is in your knowledge and the structure built around it — not in any one company's software.
You own your knowledge. That will never change.
That is a reasonable starting point. Most people's experience with AI is chatbots that make things up. This is different. The AI here is not inventing anything — it is organizing material you provided. Every summary, every connection, every answer comes from your documents. It cites its sources. If something looks wrong, you trace it back to the original.
The AI is a librarian, not an author. It shelves your books and helps you find them. It does not write them.
One question: can your current system answer a question that requires connecting information from three different sources you read in three different months?
If not — that is the gap. Your system stores. This one connects.
The setup takes a few hours, and you do not do it — I handle the entire build. After setup, your daily interaction is two things: save a file, ask a question. If you can save an email attachment and type a sentence, you can use this system.
ChatGPT starts from scratch every time. Upload a file, ask a question, get an answer. Next week, it has forgotten everything. Upload the file again.
This system remembers and compounds. Knowledge you add in January is still there in December — organized, cross-referenced, and enriched by everything you added since. The difference between hiring a consultant for one meeting and having an assistant who has been with you for years.
We talk for an hour about what you read, how you work, and what questions you need your library to answer. I design the structure around your field and your material.
I build the library and process your most important existing documents — typically 15 to 30 articles, papers, or reports. You receive a working library with real content, not a blank template.
I walk you through the two daily interactions and confirm you can operate independently. About 90 minutes.
Two to three weeks later, I check in. We review your first independent use, run a health check, and answer questions.
The entire engagement is 10 to 13 hours. The one-time build fee is $2,500.
After the build, your ongoing costs are the AI subscription you pay directly to Anthropic (the company behind Claude, the AI that maintains the library). That runs approximately $100 per month depending on how actively you use it. I size the right plan during discovery.
Optional ongoing maintenance — periodic health checks on the library's structure and content — is available as a separate monthly arrangement starting at $200 per month. Most clients find it valuable once the library grows past a few hundred articles.
This is built for people whose work depends on what they know and what they can find when it matters: physicians tracking clinical research and treatment guidelines, attorneys building case law expertise, professors managing years of academic literature, executives synthesizing board reports and strategic analyses, and any professional who reads more than they can organize.
If your career has given you a body of knowledge worth preserving and connecting — and you have never had a system that could keep up — this is what that system looks like.
I am Scott Shirey, a CPA and fractional finance director based in Breckenridge, Colorado. I built my own personal knowledge library first — for the same reason you are reading this page. I had years of professional material scattered across too many places, and no system that maintained itself. I built the system I needed, and then I realized it works for anyone whose job runs on accumulated knowledge.
I also build AI work environments for small business and nonprofit teams — a separate service, same underlying expertise. The common thread is that I understand professional knowledge work because I do it every day, and I configure AI tools around how professionals actually think, not how engineers imagine they should.
Need this for your team, not just yourself? See AI Cowork Consulting →
If this sounds like what you have been looking for, reach out. A 30-minute conversation is enough to know whether this is a fit for your work.
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