The gist: Driver uses a compiler-inspired system to pre-compute exhaustive understanding of your codebases and serve it to AI coding agents via MCP. With Driver's content, Cursor, Claude Code, and Copilot never get lost or go down the wrong rabbit holes enabling you to build fearlessly at scale.

"We spend a ton of time on the planning and research phase of our agentic SDLC — Driver is where the real value is for us. It's helping our engineers dramatically on story tasking alone, and it's literally going viral inside our company. I have to tell my engineers to hold on while we get everything set up." — CTO, Leading E-commerce Fulfillment Platform

Hi everyone, we're Adam and Daniel. We launched Driver on Bookface two years ago as a documentation tool. A lot has changed.

What actually happened in two years

We started by building a system to produces accurate, symbol-complete documentation. It became the source of truth for our customers across their engineering orgs.

This evolved quickly into AI agents reading the docs. Customers began checking our docs directly into their repos as markdown files making them available to Cursor and Claude Code.

The shift to context for agents unlocked value at a much larger scale: their agents could successfully complete tasks that they had previously failed at.

We hadn't built a documentation tool. We'd built the context layer.

The problem

AI agents fail on real codebases without context.

Ask an agent to work in a 20M line repo. It iterates with grep, ls, repeated searches. It takes forever, is incomplete, and is wildly non-deterministic. That translates to frequent hallucinations, low trust, and outright task failure.

Teams try to fix this with bigger context windows (doesn't scale), RAG methods (non-exhaustive, semantic limitations, poor scaling), or manual context gathering (burns hours per engineer per week, inconsistent and unmaintainable at scale). None of them work.

We took a different approach: pre-compute exhaustive understanding ahead of time, make this fully automated and always up-to-date, then serve it to agents through MCP.

_"The better the documentation, the better the agent. Your context is better than anything else on the market."_— VP Engineering, Leading Financial Software Provider

How it works

At the core is our Transpiler: an exhaustive compiler-inspired system combining static analysis with LLM generation. It produces:

1. Symbol-complete documentation for every file
2. Architecture overviews to guide agents and plan
3. Dependency maps for reviews
4. Commit-complete changelogs

We also build runtime sub-agents optimized to shape pre-computed context to the perfect package for a runtime task: Joint optimization between compile-time and runtime.

Every file visited, every symbol documented, relationships preserved. All kept current automatically as code changes.

We expose this compiled output through carefully designed interface primitives callable via MCP or API.

"I refactored the entire 1M line codebase with Driver and Cursor and I have no idea what it does. And with Driver, have no need to. The first thing I do is ask Cursor to research with Driver's MCP." — Head of Data Engineering, Global Trading Firm

Traction

We've deployed across 25+ enterprise customers, including high-frequency trading firms and Fortune 500 companies, processing over 200 million lines of code in the last six months.

Security

SOC 2 Type II certified. Multi-tenant SaaS, single-tenant VPC, self-hosted options, and GovCloud available.

How to get started

1. Connect your SCM: Link Driver to GitHub, GitLab, Bitbucket or any other SCM system to ingest codebases and create context.
2. IdP & SSO: Set up SSO, enable SCIM, and map IdP groups to Driver teams to provide access to context for your organization.
3. Integrate Context Anywhere: User-authenticated access via MCP server for interactive agents, M2M authentication for headless agents, and REST API for server-side access.

Our Ask

Try it on your codebase at driver.ai

• Large codebase and using Cursor/Claude Code? We'll set up MCP tools and you'll see the difference in a day.
• Enterprise with dozens of internal systems? We process all of them. Your agents get cross-repo context.
• Onboarding engineers to legacy code? Architecture knowledge stops living in one person's head.

"Within five hours, I built a functioning prototype that touched almost every area of our back end. It was supposed to take six weeks." — Director of Product, Leading Payments Platform