Writing
Context, connected.
Notes on context infrastructure: context graphs, RAG, MCP, and giving AI agents context that is current and connected.
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Context graph vs RAG: why retrieval misses how work connects
RAG retrieves the chunk that looks similar. A context graph answers from how your work connects. Here is the difference, with a side-by-side comparison.
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A Glean alternative built as a context graph, not enterprise search
Glean is enterprise search. Suda is a context graph your AI agents query directly. Here is the difference and when each one fits.
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MCP context servers: giving any AI agent your real context
MCP lets any AI agent pull context from an external source. Here is what an MCP context server is, why it matters, and how Suda serves your whole context graph over it.
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How to give your AI a single source of truth across Notion, Slack, and Linear
Your context is split across Notion, Slack, and Linear, so your AI answers from a fragment. Here is how to give it one current, connected source of truth.
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What is a context graph, and why AI agents need one
A context graph is a living map of how your work connects, built so any AI agent can read it. Here is what it is, how it differs from RAG, and why agents need one.