We build the infrastructure that removes the reasons AI agents guess, so software runs on what's real, not what's plausible.
Deterministic ground truth about your codebase, so agents stop guessing about it.
theauditortool.com ↗An open, un-gameable benchmark that measures what a SAST tool can actually find. Proof the truth layer is real, not assumed.
benchproctor.com ↗A provider-agnostic client that lets agents act safely, without burning your context budget.
wardenclient.com ↗Persistent memory that ranks by truth and runs on your own GPU.
curatormcp.com ↗The command center that dispatches, routes, and recovers your whole AI operation.
arbitermcp.com ↗What we build
An agent is only as good as what it knows. Left to guess, it hallucinates, drifts, and ships things you never asked for. Everything we build removes a reason to guess.
Correct
Grounded in your real code and real history, not a plausible guess.
Compact
Facts, not prose dumps. Context budget spent on the work, not the wrapper.
Persistent
Knowledge that survives the session, the reboot, and the next model.
Verifiable
Claims you can measure and supervise. Proof, not vibes.
Why one House
We didn't set out to build five products. We set out to stop AI agents from guessing, and each layer we fixed exposed the next missing one. That's why the stack is coherent instead of scattered, and why it lives under one roof.
Correct
High-quality context needs a deterministic understanding of your code. Build that deeply enough and you have TheAuditor.
Proven
A context engine is only worth trusting if its claims are measurable. That demanded an un-gameable benchmark. That is BenchProctor.
Used well
Even perfect context is wasted by clients that drown it in prose. So we built one that treats facts as facts. That is Warden.
Remembered
Current-code truth is still amnesiac. The agent also needs history, preferences, and what changed. That is Curator.
Orchestrated
Long-running work needs supervision and routing above all of it. That is Arbiter.
The unfair advantage
Any one of these is a strong standalone tool. Run them together on one workstation and they stop being five products and become a single system. Each one discounts a different factor of the same bill, so the cost pressure compounds. Correctness becomes a closed loop: the proof layer scores the truth layer, the truth layer feeds the agents, and memory keeps them from re-making a decision they already settled.
Provable correctness
An honest score you can reproduce, not an unverifiable recall claim. Public labels, balanced cases, and rotated releases are designed to resist leakage and overfitting.
Cross-boundary provenance
Deterministic results and data-flow paths that cross language, process, queue, and infrastructure edges, where file-bounded analysis loses context.
Evolutionary memory
Knowledge that survives corroboration, decay, and supersession, and applies what changed. Not a frozen vector lookup that returns the nearest stale match.
Local sovereignty
A free local tier, storage encrypted at rest, no listening port, and irreversible actions approved with a code from your phone.
Run one and it stands on its own. Run all five and the value compounds: a self-correcting, cost-aware, memory-carrying engineering system that lives on hardware you own and stays under operator control.
“Five products was never the plan. I set out to fix one thing, an AI that builds on what's real instead of what it guesses, and each fix uncovered the next. None of this was designed as a suite. It became one because it all came from the same necessity, and that is why the pieces fit the way they do.”
Code Reality Labs · Founder note
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