Buying guide
How to choose an AI context layer
An AI context layer is what turns a capable model into a useful colleague — shared organisational memory every tool can read. As a new category, it’s easy to mis-buy. Here’s what matters.
Where does the context come from?
Some “context” tools ask you to maintain a knowledge base by hand — which goes stale like any wiki. The stronger model captures context automatically from where work actually happens: your meetings, threads, and email.
- Is context captured automatically, or maintained manually?
- Does it stay current as decisions change?
- Is it structured, or just a pile of documents?
Open standard, or lock-in?
The whole point of a context layer is that every tool can use it. Favour platforms built on MCP (the Model Context Protocol) so Claude, Copilot, ChatGPT, and Cursor all draw from one source — rather than a proprietary integration you’ll have to rebuild.
Governance and least privilege
Shared context must not become a leak. Check that access is scoped — workspace boundaries, role-based access, no training on your data — so each tool and agent sees only what it should.
How the main options differ
Most tools people weigh up here started somewhere else and added context as a feature. AI assistants answer from what you attach; enterprise search retrieves what was written; workspace AI reasons over docs you maintain. A purpose-built context layer captures decisions and commitments automatically and serves them to all of them.
- ChatGPT Projects — context you attach and maintain, inside one assistant
- Microsoft Copilot — AI over your Microsoft 365 content
- Glean — search across what your organisation has written
- Notion AI — AI over the workspace your team keeps current
- In Parallel — memory captured from meetings automatically, served to every tool over MCP
FAQ
Common questions
- What is an AI context layer?
- Shared organisational memory that every AI tool can read, so a capable model works from your real business context instead of a blank slate.
- How should the context be captured?
- Automatically, from where work happens — meetings, threads, and email — so it stays current, rather than a knowledge base you maintain by hand that goes stale.
- Why does MCP matter?
- MCP is an open standard, so every tool can use one context source. It avoids proprietary lock-in you would have to rebuild for each new AI tool.
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