Retrieval without
approximation.

A white-box audit layer for AI memory, and deterministic reasoning.

Every step an AI takes is logged as an exact trace you can audit, replay, and re-derive. Built for the people who answer for what the AI did - investors, auditors, and regulators.

exact replay
Every decision replays.
Same result, any machine.
white box
Every step visible.
Reasoning you can read.
closed loop
Every trace is training data.
Learns from its own review.
Model-agnostic — runs with any model
What it is

Intelligence has
an address.

A white-box layer any model can stand on. It logs the AI's reasoning as an exact trace, so any decision can be audited, replayed, and checked instead of taken on trust. In the EU, high-risk AI must now keep automatic, traceable decision logs.

Written to an exact address

Every step of reasoning lands at a fixed address - by construction, not by similarity search. The log is exact, and the log is the audit trail, Hallucination-free, fully governed.

Replay to verify

Replay any decision and it re-derives to the identical result, on any machine. You verify by re-running it, not by trusting it. Auditable, and shareable across teams.

Test a what-if

Change one input and replay to see how the reasoning would resolve. The original record never changes. It's a re-runnable object, not just a receipt - diagnostic, not only historical.

How it works

Coordinates hold
your answers.

No training. No labeled data. No model needed.

01

Data arrives

Each record is mapped onto a deterministic coordinate space — its address computed on a cryptographic foundation the industry already trusts. The position encodes meaning.

02

Query arrives

The query navigates straight to the answer's address, by utilizing neuro-symbolic shapes, not by comparing vectors.

03

Result is exact

The position is the index. No rebuilding, no drift. Every input is checked against the axioms on the way in, so the system catches and corrects its own errors on the reasoning and index layers.

 
Continuous learning — Roadmap

Reasoning that trains
the model.

The audit log is also a clean training signal. Every recorded decision is verified reasoning - the kind of data an open-weights model can actually learn from.

Where we're headed: models that improve from their own audited reasoning in real-time, instead of a separate versioned retraining run. Verified first, learned second. Longer term, many deployments feed verified reasoning back to shared open-weights models - a federated loop.

Invite list

Soft launch.
By invitation.

List members get priority access in the first cohort, launch terms locked in, and a direct line to the team. Investors and audit partners: a direct line to discuss access and evaluation.

Status
Private beta · invite only
Terms
Locked for list members.
Access
Rolling, in cohorts

No spam. One announcement at launch.