Retrieval-augmented generation (RAG) is a technique where an AI system fetches relevant documents or data at query time and places them in the model's context, so its answer is grounded in real sources rather than parametric memory.
RAG addresses the fact that a model's weights are frozen and lossy. Rather than rely on what the model "remembers," the system retrieves pertinent material — via search, a database, or a vector store — and supplies it as context, so the answer reflects current, specific, verifiable data.
Tool-calling and MCP are increasingly the retrieval layer: instead of pre-indexing everything, the agent calls a live tool for exactly the data it needs, when it needs it.
Rather than recall a fund's holdings from training data (and risk inventing them), a RAG agent calls a live tool and answers from the returned rows.
Arkolith is the retrieval source: a live, sourced, queryable data layer is exactly what a RAG or tool-using agent needs to stop guessing about markets.
Arkolith turns this into live, sourced data your agent can query — SEC filings, insider activity, and market data behind one key, every datapoint traceable to its origin.