The short version
Claude, ChatGPT, Copilot and your own agents all know who decides what, what was decided, what you sell and how the work runs. They know it because they read the same source. Not because someone pasted it in this morning.
The word universal carries the point. Every tool your people use reads the same context, and every answer starts there. The home page draws it as one picture: every tool wired into one knowledge base.
What goes in it
Universal AI context holds what a new colleague would need to know. It also holds what nobody ever wrote down.
- People. Who works here, in which role, and who owns what.
- Decisions. What was decided, when and by whom, and the reasons behind it.
- Products and services. What you sell, and to whom.
- Brand and voice. How you sound, so a draft sounds like you.
- Processes. How the work runs, and where everything lives.
- Files. The documents you already have, and what they say.
It starts with a guided interview, or with the documents you already have. From there it grows as you work. Meetings, mail and documents land in the same place. When something is wrong, you correct it once. It stays corrected for everyone who asks after you.
Two things people call a context layer
A context layer sits between an AI model and an organisation, so the model knows what it is working with. People use the words for two different things.
In analytics, a context layer describes data: what a table holds, how a metric is calculated, which number is the official one. It helps an AI write a correct query against your numbers.
The other kind is about how an organisation works. It holds people, decisions, products and processes, in words a person can read. An AI tool uses it to answer the way a well-informed colleague would. That kind is universal AI context.
| A data context layer | Universal AI context | |
|---|---|---|
| What it describes | Tables, metrics and how a number is defined | People, decisions, products and processes |
| Who uses it | An AI that writes a query | The AI tool of every colleague |
| A question it answers | What was revenue last quarter? | Who decides on a discount, and what did we agree? |
| Who keeps it current | The data team | Everyone, as they work |
| Can they live together | Yes | Yes |
The two can live side by side. One tells an AI what the numbers mean. The other tells it who you are.
It is not one AI tool's memory
Most AI tools remember something now. A chat keeps a memory. One AI tool's projects hold files and instructions. That helps one person, in one tool.
It stops there. What one tool's project holds, another tool does not read. What you taught your AI, a colleague's AI does not know. Switch tools, and you start again. A personal second brain in a note tool has the same edge, which is why a team needs a company brain, not just a second brain.
Universal AI context sits outside any single tool. Claude today, ChatGPT tomorrow, Copilot at work: the same context in all of them. It belongs to the organisation, not to one person's account. What that takes is on AI memory for a whole team.
One source of truth for every AI tool
Teams do not use one AI tool. Sales works in one, finance in another, operations in a third. Each tool on its own gives each person a different picture of the same organisation, and each person explains the organisation again: your AI starts every conversation from zero.
One source changes that. What was decided in a chat in Claude is there when a colleague asks in ChatGPT. The answer carries its source, so anyone can check it. And when something changes, it changes once, for everyone.
People call that a single source of truth: one place that holds the current version of a fact. For AI it means every tool reads that place, instead of what each person happened to paste in.
It also keeps you free. Your context is not locked into the tool you picked this year. Change tools whenever you like, and the context stays.
Other words for nearby things
Universal AI context is the term Dienox uses. These are the words people search with.
- Company brain. The word for the need: what an organisation knows, shared, where every AI can read it. What is a company brain?
- AI second brain. One person's notes, read by that person's AI. A company brain is the shared version. What is an AI second brain?
- LLM wiki. A wiki that an AI model writes and keeps current from your sources. Universal AI context is one for a whole company: an LLM wiki for your whole company.
- Context engineering. The work of giving an AI model the right information for a task. Anthropic's engineers describe it in Effective context engineering for AI agents. Universal AI context does that work once, for everyone.
People also look for it as universal context, without the AI. Several companies use those words for products of their own. On this site, universal AI context always means the definition at the top of this page.
What Dienox provides
Dienox is a platform that gives a company one knowledge base that every AI tool its people use reads from: Claude, ChatGPT, Copilot, Cursor and any other MCP client. MCP is the open standard those tools use to connect. The glossary defines it, with the other words on this page.
- One source. A knowledge base of your organisation, with the history of every change.
- Every tool. Each person adds Dienox in their own AI tool as a connector, signs in and picks the organisation. About a minute per person, and nothing to install.
- Your limits. Roles decide who may change what: administrator, editor or reader. Every action leaves a trail.
- Yours to keep. Your knowledge is kept in open files, with its full history. Leaving means taking all of it with you.
On the same source, agents take on recurring work: the weekly overview, inbox triage, meeting preparation. Each one only gets the actions you switch on for it: agents that cannot do what you did not allow.
Questions people ask
What is universal AI context?
Universal AI context is the knowledge of an organisation, kept in one place and read by every AI tool its people use, so every answer starts from the same facts. Claude, ChatGPT, Copilot and your own agents all know who decides what, what was decided, what you sell and how the work runs. Dienox is the platform that provides it.
What is a context layer?
A context layer sits between an AI model and an organisation, so the model knows what it is working with. People use the words for two things: a layer that describes data, such as tables and metrics, and a layer that describes how the organisation works. Universal AI context is the second kind, read by every AI tool.
Is universal AI context the same as a context layer?
It is one of the two things people call a context layer. In analytics, a context layer describes data: tables, metrics and how a number is defined. Universal AI context describes how the organisation works: people, decisions, products and processes. The two answer different questions, and an organisation can use both side by side.
Is it the same as a company brain?
Yes, in other words. Company brain is the word people use for the need: what an organisation knows, shared, where every AI tool can read it. Universal AI context is the term Dienox uses for its answer to that need, with roles, the history of every change and a source under every answer.
How is it different from the memory in ChatGPT or Claude?
The memory in an AI tool belongs to one person, in that one tool. A colleague's tool does not share it, and another AI tool does not read it. Universal AI context sits outside any single tool and belongs to the organisation, so Claude, ChatGPT and Copilot all start from the same facts.
Is it the same as RAG?
No. RAG is a technique: passages from your documents are looked up in a search index at the moment of a question. Universal AI context is the knowledge itself: what the organisation knows, written down, kept current and read by every AI tool. One is a way of finding text. The other is what there is to find.
Which AI tools can read it?
Claude, ChatGPT, Copilot, Cursor, Windsurf, Gemini CLI and any other tool that speaks MCP, the open standard for connecting AI tools to data and systems. Each person adds Dienox in their own tool as a connector, signs in and picks the organisation. It takes about a minute, and there is nothing to install.
Who owns the context?
The organisation. It is not tied to one person's account or to one AI tool. In Dienox your knowledge is kept in open files with its full history, and leaving means taking all of it with you. Change AI tools whenever you like: the context stays, and the new tool reads it.
How does it start?
With a guided interview: a tested set of questions that draws out what normally lives in people's heads. Half a day, and the knowledge base has its first version. Or start from the documents you already have. Dienox reads them, turns them into a first version and lists the questions it could not answer.