Make AI agents work on complex codebases.
We build a structured context layer for your codebase and demonstrate it improves agent performance on your actual tasks.
3-month project delivered in 12 days.
3 repos • 200+ features • 14 data sources
RobloxDoubled AI code acceptance — 30% to 60%.
Not a better model — better context.
Research
3.3x improvement in agent accuracy.
Structured context across 300+ open-source repos.
The Dark Room.
Every agent starts from zero. Every task is a full onboarding — not just to your task, but to your entire system.
Your documentation problem just became your throughput bottleneck.
Turn on the lights.
The Intent Layer captures your team's institutional knowledge and makes it available to every agent, on every task, automatically.
Your agents start every task with your team's institutional knowledge.
What your team gets.
Agents find the right files
No hallucinated imports or missed dependencies. Agents navigate your codebase like a senior engineer.
Complex tasks land
Cross-service refactors, data migrations, feature builds across modules — the tasks that used to fail.
Ramp time collapses
New engineers and new agents get your team's institutional context on day one. Not month three.
Context compounds
VCS hooks keep the Intent Layer current as your code evolves. Every sprint makes agents smarter, not staler.
Prove it on your code. In 48 hours.
We don't ask you to trust a pitch deck. We measure the difference on your actual codebase, your actual tasks.
We build the context layer
Read-only repo access → structured Intent Layer in 48 hours.
We run the eval
Real tasks from your backlog. Agents execute with and without the Intent Layer.
You see the results
Benchmark report on YOUR code. No improvement, no charge.
Why we benchmark on your code, not ours. Results vary. On some tasks, the improvement is modest. On others — especially where institutional context is the difference between success and failure — the Intent Layer is the difference between an agent that completes the task and one that never gets close. That variance is exactly why we run the eval on your code first.
Proof Pilot: $20K per 1M tokens mapped + onboarding and eval.
No improvement on your eval, no charge. You keep the Intent Layer either way.
Your code stays yours.
Your LLM provider, your keys
Code is processed through SOC2-certified model providers (Anthropic, OpenAI) that don’t train on API data. We can use your API keys and your preferred provider.
Run in your environment
For security-sensitive codebases, we run the cartographer entirely within your infrastructure. No code leaves your network.
Start a Proof Pilot.
We'll build the context layer, run the eval on your actual tasks, and show you the results in 48 hours.
$20K per 1M tokens mapped • No improvement, no charge • You keep the Intent Layer either way