Friday Update: What does AI transparency mean?

Hi, I'm Anthony from Mevanu. In our Going Solo update, we followed an experienced copywriter exploring a business of her own. This week, we're looking at the work behind that experience through a practical question: what does AI transparency mean?

For someone using an AI assistant, it should be practical. You should be able to understand what goal the assistant is working toward, which information it is using, and what a result tells you about your progress. Brava, our AI work partner for Mac, is in development.

Picture our fictional Going Solo copywriter returning to a client draft. She has changed the audience. The earlier research may still be useful, but her new direction should guide the next step. She should be able to see which Brief applies and how the next piece of work connects to the choices she has already made.

That experience brings together two parts of Brava's current build.

The first, GK1, connects the Brief, previous work, recorded choices, results and feedback. The aim is a connected record you can inspect when you need it, while the conversation stays focused on your work.

The second, GK2, connects that record to your current intention and, eventually, to an assessment of what helped. Together, they support continuity with a purpose: carrying relevant work forward while respecting your latest direction.

This week brought a concrete step. In a controlled native test, the selected current Brief and intention reached the step that refines a request. The draft and selected context were preserved. That builds on earlier checks of saving and reopening intention and history.

The intended benefit is less repeated explanation and a clearer connection between what you asked for and what happens next. The test establishes that bounded connection. The complete everyday experience, using real services, still needs qualification.

We also checked a smaller detail with a larger consequence. In a controlled display test, hidden, minimized, off-screen or stale advice did not count as presented; the current visible state did. A later assessment should distinguish advice that was presented from advice you never had a chance to consider. Being visible still doesn't establish that you read it, chose it or benefited from it.

That is why outcome evaluation matters. Finishing a draft, accepting a suggestion and making progress toward your goal are different things. Brava's evaluation work must keep those distinctions intact. Measured usefulness remains ahead of us.

The full GK1 journey also remains open. Our latest diagnostic has passed independent source review, and its signed build has now passed on M1. The new diagnostic files compiled and the assembled candidate preserved its signatures. Independent artifact review and the native behavior checks are the next steps. That will help us investigate where the connected journey stops.

We also ran two of our development conversations through Paxel today. Its automated assessment called us “The Architect” both times. We’ll take it.

Next, we're taking that signed diagnostic through independent artifact review before the separately approved native checks, continuing the connected evidence journey, and working toward goal-bound outcomes and independent evaluation. The user-facing aim is clear: relevant context, understandable choices and an honest account of what the work achieved.

So, what does AI transparency mean? For us, it means understanding the goal and context behind the work, being able to inspect the relevant choices and results, and seeing clearly what remains uncertain. We still need the complete everyday journey and independent outcome evaluation before we can honestly say how well Brava delivers that experience or how much it helps.

Have an idea? Drop a prompt, comment, suggestion, or question.

Mevanu. Just. Smarter.