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Compiles a codebase into architecture docs, symbol tables, and dependency maps that AI coding agents query over MCP or an API.
Driver analyzes a codebase ahead of time and produces structured context — architecture documentation, a symbol table, and dependency tracking — that AI coding agents can query instead of re-reading raw source on every request. Teams reach it through an MCP server compatible with Claude Code and Cursor, or through a REST API with machine-to-machine authentication. It supports multiple repositories and branches and targets engineering, support, and QA teams running AI agents at a scale where ad hoc prompting into a codebase stops working. Pricing is usage-tiered by lines of code analyzed per year and is not self-serve; you have to talk to sales to get a quote.
No self-serve price is published; plans are tiered by source lines of code analyzed per year and quoted by sales, with LLM token usage billed separately.
Use tool ↗Driver is infrastructure for AI coding agents rather than a tool developers use directly: it pre-compiles a codebase into documentation and context an agent can query over MCP or API. That can make agents faster and more accurate on large codebases, but there's no visible self-serve price, and the entry tier already assumes millions of lines of code per year.
Watch out: The lowest published tier starts at 5 million lines of code analyzed per year, so it is built for teams above a certain codebase size rather than individual projects.
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