A faster start
Begin with a source-linked draft instead of a blank page or a search across five folders.
Governed AI for regulated work
Move regulated work faster. Keep control of the evidence.
GAIA Brain is a customer-hosted control and evidence layer for approved AI-assisted work. It helps regulated teams use firm knowledge while keeping access, egress, sources and human review visible.
Source code and internal test evidence show the solution is just short of MVP. No official customer deployment; design-partner pilots are being prepared.
Your team is already choosing between slow work through approved processes and fast, undeclared and uncontrolled AI use.
A policy alone does not remove that trade-off. The useful alternative is a controlled work path: the right user sees the right sources, external egress is an explicit decision, outputs carry evidence, and a named professional remains accountable.
From source to accountable work
The product is designed around the whole transaction, not only the model call. The first proof is a small number of expensive workflows with measurable before-and-after results.
- 01
Authorise
Establish the user, task and knowledge the work may use.
- 02
Control
Apply access and configured egress rules before model dispatch.
- 03
Assist
Produce a source-linked draft, uncertainty or a safe refusal.
- 04
Review
A named professional corrects, approves or escalates, leaving a trace.
The protected-egress mechanism is built but remains gated for witnessed activation and customer validation. The current product is not generally available.
Why a regulated team should care
Less key-person dependence
Reuse approved knowledge without pretending that professional judgment can be automated.
Visible exceptions
Make uncertainty, refusal, correction and escalation part of the work rather than hidden model behaviour.
Customer control
Run the environment yourself or through an approved partner, with deployment and go-live responsibilities stated plainly.
Bring one expensive workflow and one skeptical reviewer.
We will define the sources, data boundary and acceptance measures, then let a paid, bounded pilot decide whether the product earns a place.
Start with the workflow