A context layer is the infrastructure that sits between your company's knowledge and the AI tools your team uses. It ingests your documents, structures them into searchable knowledge and a graph of entities, governs what each answer can include, and serves that context to Claude, ChatGPT, or any AI client — so the AI answers from your business, with citations, instead of guessing.
Context, not chatbot
A context layer is not another AI assistant — it sits underneath the AI your team already uses. The model still writes the answer; the context layer makes sure the answer is grounded in your company's real, current, permission-checked knowledge.
What it does
A context layer does the unglamorous work that turns a pile of documents into something an AI can answer from reliably.
- Ingests your sources — Notion, Drive, email, uploads — and keeps them current.
- Structures every document: cleaned, classified, summarised, and embedded for semantic search.
- Maps the business into entities and relationships — people, clients, projects, products.
- Governs retrieval — sensitivity filtering, citations on every answer, and an audit log.
- Serves it all to any AI client over a standard protocol, so one layer feeds every tool.
Why the term is appearing now
As teams moved past “chat with one document” to running real work through AI, the missing piece became obvious: the AI was powerful but knew nothing about the business. The context layer is the answer — and MCP, the open Model Context Protocol, is what makes one layer usable by Claude, ChatGPT, Cursor, and whatever comes next.
A context layer for small and mid-sized firms
Most “context layer” talk is aimed at large enterprises and priced to match. OpsBlox brings the same idea to knowledge-driven SMEs — consultancies, agencies, advisory firms — with governance built in, pricing by sources rather than seats (from R399/month), and the option to run it in South Africa or on a box in your own office.
Common questions
How is a context layer different from a knowledge base?+
A traditional knowledge base is for people to read. A context layer is for AI to query — it structures and governs the same knowledge so an AI client can retrieve exactly the right, permission-checked context at the moment a question is asked, and cite where it came from.
Is a context layer the same as RAG?+
RAG (retrieval-augmented generation) is one technique a context layer uses. A context layer is broader: it adds connectors, a knowledge graph, approved memory, and governance on top of retrieval, and serves it to any AI client over MCP — not just a single app.
Do I need a context layer if my team only uses ChatGPT?+
If you only ever ask the AI general questions, no. The moment you want it to answer accurately about your own clients, projects, and documents — cited and governed — that's exactly what a context layer provides, and it'll already be there when you add a second AI tool.