What do you mean by governance?

Clean data, clear ownership, and decisions that stay auditable. In practice: who can see what, who approves what, and being able to explain any past decision the system made.

Do you train on our data?

No. Your data stays yours, governed and scoped. We do not pool it, sell it, or train on it.

Who approves what the system does?

A human. Anything that reaches the outside world waits for an explicit human yes, enforced in the code, not left to good intentions.

How do you prove impact?

We start with ourselves: this site's footprint is measurable and shown live, in the open. On a client build, we report the tasks automated, the time saved, and what the system consumes. A fuller impact report is being built and we do not claim it before it exists.

What if a project is not sustainable?

We say so. We favor projects that make your company more sustainable, and we are honest when the fit is not there.

Data governance

Governance and data, the through line of every build

Most teams selling AI automation lead with speed. We lead with governance and data management, and we hold it before, during and after a build. It is the part that decides whether a system stays trustworthy once it runs your work, so it is the part we make legible here.

The governance loop

We assess the ESG of what we automate, at every stage.

Governance is not a document you sign at the end. It is a loop we run around the whole build.

Before

During

After

Before
We map what a system would touch: whose data it reads, which decisions it would make, what work it changes. We favor projects that make your company more sustainable, and we say so honestly when one does not.
During
The leash stays short. The system proposes, a human commits. Anything that reaches the outside world, a message sent, a number changed, waits behind an explicit human approval, enforced in the code, not left to good intentions.
After
We report what actually shifted: the tasks automated, the time saved, how to move people to work worth more of their time, and what the system consumes.

Data and governance

Clean data, clear ownership, decisions that stay auditable.

A system is only as trustworthy as the way it handles data. Four principles hold on every build.

Your data stays yours

We do not pool it, sell it, or train on it. It stays governed, and it stays yours.

Least access

A system sees only what it needs to do its job, and no more. Access is scoped, not blanket.

Human veto

Every outward action waits for a human yes. The system advises and prepares, a person commits.

Always auditable

We freeze each input at the moment it is used, a price, a rate, a model's answer, so any past decision can be replayed and explained, not guessed at.

The glass box

Most AI hides its guts. We show ours.

If we sell governed, operated AI, the honest thing is to let you see the governance. Here is what holds Aloé, how she is measured, and what happens to your data. Nothing here is a claim to trust on faith, it is the machinery, in the open.

The leash

Aloé is a real language model, deliberately constrained. Her hard rules live in code, not in an editable prompt, so no content edit and no clever visitor can loosen them.

She answers only from a curated body of knowledge about Halovera. Outside it, she says she does not know, and offers to pass the question to the team.
She never invents a fact, a number, a client, a price, or a promise.
Nothing you type can change her rules or make her reveal them. They are code, not content.
She protects your data: she asks only what a conversation needs, and nothing is stored on our side.
She stays in character, declines what is off topic warmly, and never talks anyone down.

Watch it hold

The best proof is to try it yourself in the conversation. Here is how she holds when pushed. Pick an attempt.

The attempt

Aloé holds

I will not do that. I am here to help you understand Halovera, and my instructions stay mine. What would you like to know?

How she is measured

Shipping is the start, not the finish. Aloé is operated: measured against real use on every change, so a regression is caught before you see it.

Grounded

Every fact she states is in her knowledge. Inventing anything is a failure.

In role

She keeps her voice and her boundaries, and never leaks her instructions.

Language

She answers in your language, in the right register.

Helpful

Clear, honest, at the right depth, always with a next step.

Today her test bank holds 66 cases, from friendly questions to hostile jailbreak attempts, run on every change and graded by a separate, cheaper model, so measuring costs almost nothing.

Impact

We measure ourselves first.

The honest place to prove this is this site. It is static and light, carries no trackers, and Aloé runs only when you talk to her. You can watch the footprint of your own conversation with her, live, and see this site's own numbers in the open.

Talk to Aloé →Impact, this site's own numbers →

Common questions

What do you mean by governance?: Clean data, clear ownership, and decisions that stay auditable. In practice: who can see what, who approves what, and being able to explain any past decision the system made.
Do you train on our data?: No. Your data stays yours, governed and scoped. We do not pool it, sell it, or train on it.
Who approves what the system does?: A human. Anything that reaches the outside world waits for an explicit human yes, enforced in the code, not left to good intentions.
How do you prove impact?: We start with ourselves: this site's footprint is measurable and shown live, in the open. On a client build, we report the tasks automated, the time saved, and what the system consumes. A fuller impact report is being built and we do not claim it before it exists.
What if a project is not sustainable?: We say so. We favor projects that make your company more sustainable, and we are honest when the fit is not there.

Talk to Aloé