Write one canonical context file — company, clients, decisions, style, current state — and make every AI tool read that same file instead of re-briefing each one. That removes most of the repetition today, for free. Manual files then drift, so the durable fix is a shared layer that keeps the same facts current from the tools you already use.
Start here: the context file
Do this before you evaluate any product. It takes about half an hour and it will tell you how much of the problem is actually repetition versus staleness.
Create one file. Paste it into each AI tool at the start of a session, or attach it as a project instruction or custom-GPT file. Five sections:
# COMPANY
What we do, in one sentence. Who buys it. How we make money.
Team size and who does what. Anything an outsider would get wrong.
# CLIENTS
One line each: name, what we do for them, current status,
and the single constraint that matters most for that account.
# DECISIONS
Decisions already made, with the reason. Include the ones that
were reversed and why. This is the section that saves the most
time, because it is what you re-explain most often.
# STYLE
How we write. What we never say. Formatting preferences.
Names for things that we use differently from the industry.
# CURRENT
What is in flight right now, and what is blocked on whom.
This is the section that goes stale fastest — date it.
Two rules make the difference between a file that helps and one that gets ignored.
Write decisions, not background. "We moved off per-seat pricing in March because enterprise buyers kept stalling on headcount forecasts" is worth ten paragraphs of company history. A model needs the constraint and the reason, not the narrative.
Date the CURRENT section. It is the part that rots. If it carries a date, a reader — human or model — can tell how much to trust it. If it does not, an eight-week-old status line reads exactly like a fresh one.
Where the manual file breaks down
The template above solves repetition. It does not solve staleness, and staleness is the harder half. Four failure modes, roughly in the order you will hit them:
Drift. The file is a snapshot. The moment a decision changes, the file is wrong — and unlike a stale conversation, a stale context file is authoritative, so the model states the wrong thing confidently.
Update cost. One file becomes four copies: the ChatGPT project, the Claude Project, the editor's rules file, the doc you keep for yourself. Updating a decision means finding all four. In practice you update one, and the others quietly disagree from then on.
No access control. A context file is all-or-nothing. Bring in a contractor and you either hand over the file containing every client's commercial constraints, or you maintain a second, redacted copy — which then drifts from the first.
No sources. The file asserts. It cannot show you that the annual-billing constraint came from a specific email on a specific date, so nobody can check a line that looks wrong, and nobody can tell a current fact from one that expired.
There is a fifth, quieter one: writing to the file is manual. Every decision reached in conversation has to be transcribed by you, later, from memory. Most of them never are.
What a shared layer changes
The pattern is the same — one canonical set of facts, read by every tool. What changes is who maintains it and what each fact carries with it.
- One store, many clients. The facts live in one workspace that every AI client reads over Model Context Protocol or a connector, instead of in four copies. Updating a decision updates it everywhere, because there is only one of it.
- Capture without transcription. A decision reached in a Claude conversation can be written to the workspace from that conversation. It does not depend on you remembering to update a file afterwards.
- Facts derived from tools you already use. Connecting email, chat, documents or code means the content in them becomes searchable context, so the CLIENTS and CURRENT sections stop being things you maintain by hand.
- Sources travel with facts. Retrieval returns where a fact came from, so a line that looks wrong can be checked against the original record rather than argued about.
- Scope is a property of the store. Workspaces are isolated at the database layer, so the contractor question becomes an access question rather than a second redacted file.
How GreatArrow.ai does it
GreatArrow.ai is a workspace your AI clients connect to as an external tool. Claude, ChatGPT, Cursor and other MCP-compatible clients point at one endpoint and read and write the same facts. Setup is at /install, and Share Context Across Claude, ChatGPT and Cursor walks the three-client setup end to end.
The five sections of the template map onto things the workspace holds rather than a document you edit. DECISIONS become records written from whichever client you were in when you made them. CLIENTS and CURRENT can be derived from connected sources — email, chat, documents, issues — rather than typed. STYLE and COMPANY are stable enough that they stay as things you state once.
Stored content and connected-tool credentials are encrypted at rest with AES-256-GCM, and access keys are stored as one-way fingerprints, shown once, and revocable immediately. The full statement is on the security page.
A concrete example
Northwind says on a call that they will not accept monthly billing — annual, invoiced only.
With the manual file, you finish the call, and either you remember to open the file and add a line to CLIENTS, or you do not. If you do, you then update the copy in ChatGPT and the copy in Claude, or they disagree from that moment on.
With a shared layer, you tell whichever assistant you are already in: "Remember that Northwind will not accept monthly billing — annual, invoiced only, they said so on the 3rd." It is written to the workspace. When you draft the proposal in a different tool two days later, that tool finds the constraint, uses it, and can show you where it came from.
The difference is not that one is smarter. It is that the second one captured the fact at the moment it existed, in the tool you were already using, and made it readable from the others.
Who this is for, and who it is not for
Start with the template if you use one or two AI tools, or you have never written the context down. Most of the pain goes away and you learn what actually needs to be in it.
A shared layer earns its place if you use three or more clients, work with several clients or projects at once, need different people to see different things, or keep discovering that two tools believe different things about the same account.
Neither is for you if your work is genuinely stateless — one-off questions with no continuing context. There is nothing to re-explain, so there is nothing to fix.
Where to go next
Give ChatGPT and Claude Company Data With Citations covers pulling the CLIENTS and CURRENT sections from Gmail, Slack, Drive and Notion instead of maintaining them. Shared Memory Across Claude, ChatGPT, Gemini and Cursor sets out which clients are supported and what the limits are.
For the category view — built-in memory, memory libraries, enterprise search, and where each beats the others — see the comparison page. Plans and limits are on pricing; the capability list is on features.