I’m a former consultant and startup founder.

That means meetings. A lot of them.

Customer calls. Investor updates. Legal calls. Partnership talks. Product reviews. Different stakeholders. Different context. Different promises made in passing.

The hard part was not joining the calls. The hard part was staying on top of what had already been said.

I tried the tools everyone was talking about

As a venture builder, I started testing AI note-taking tools early. Granola, Jamie, tl;dv and others. I liked parts of them. The value was obvious. Better notes. Less manual admin. More context after the call. [1]

But the more I used them, the more one question stayed in the back of my mind.

Where does my meeting data actually go?

The privacy work became its own job

A lot of my calls are confidential. Fundraising. Legal setup. Customer discovery. Partnership talks. Sometimes people are open because they believe the room is small. A transcript changes that feeling.

I also had the awkward version of the problem. A meeting bot entered a call before I was there and asked for permission. It was trying to do the right thing, but the experience felt off. Suddenly the first thing in the room was software asking people whether it could listen.

After that, I spent too much time reading privacy policies.

Where is the audio stored? Is the transcript kept? Can de-identified data be used for training? Which third-party models or processors are involved? What happens when the policy changes?

Granola says third parties such as OpenAI or Anthropic cannot use personal data for model training, while de-identified data may be used by Granola for internal AI model training unless users opt out. [2] Jamie and tl;dv state that customer data is not used to train AI models. [3]

That matters. Still, as a founder, I do not have the time to dig through every detail of a cloud-based privacy policy before every sensitive call.

I need the architecture to make sense from the start.

Companies had the same concern

This came up again and again in conversations with companies.

They wanted AI meeting notes. The value was clear. Better memory. Better follow-up. Less manual admin.

But compliance, client confidentiality and internal security rules slowed everything down. TechRadar reported that privacy concerns and data security risks were among the most common objections to AI note-takers. [4] Reddit threads show the same pattern: people like the usefulness, but worry about bots, permissions and where the data goes. [5]

That became the starting point for Weeve

We built and tested our own version around a simple idea: meeting intelligence should start on your device.

No meeting bot joining the call. No raw audio sent to the cloud by default. No transcript sitting on someone else’s server by design.

Just a private way to capture, understand and use the context from your meetings.

The first version was rough. But the feeling was different.

I could record a customer call, get useful notes and still feel comfortable about the conversation I had just captured. No second tab with a privacy policy. No wondering whether I had created a compliance headache for later.

That is when Weeve became more than an AI note-taking experiment for us.

It became a way to stay prepared without giving up control.

The product we needed ourselves

For people who live in meetings, the value sits beyond a prettier transcript. It is being able to remember what matters, follow up properly and walk into the next conversation with context.

That is the product we needed ourselves.

So we kept building.

Sources

[1] Granola | Jamie | tl;dv | Zapier AI meeting assistant comparison

[2] Granola Privacy Policy

[3] Jamie Security | tl;dv

[4] TechRadar on AI note-taking adoption and privacy concerns: | TechSpot on AI notetakers, etiquette and privacy

[5] Reddit r/sysadmin thread on trustworthy AI meeting recorders: | Reddit r/msp thread on AI meeting notetakers and privacy/security concerns