Former Plaud exec raises $26M for AI app that listens to you in the background

One of the core promises of AI is that the more it knows about a person, the better it will be able to help them with tasks and make appropriate suggestions, which, if you believe the tech companies, required Apps and devices to listen, watch or record the activity of users all the time and gather all possible context.

There are obvious privacy and data security concerns with anything that continuously collects data about you. But startups build such apps undaunted anyway, armed with the belief that the market for personalized help is big enough to swallow such concerns.

Former Plaud exec Peter Mo is building Ambi to do something similar. The app aims to provide people with real-time intelligence and insights by listening all the time.

The company said on Monday that it has raised $26 million across multiple funding rounds led by Luminous Ventures, which has backed companies such as Meituan and Unitree Robotics, and original design manufacturer (ODM) Huaqin.

Ambi also releases apps for iPhones, Macs and Apple Watch. These apps act like meeting notes, but users can choose to listen so they can capture context throughout the day.

Meeting note companies have worked to automate tasks that are performed after calls, based on the summaries and action items they collect during the conversation, and with many connectors to software systems. Ambi wants to take this a step further by having his agent start the work himself during the meeting.

The company’s promise is that its AI agent can proactively give you suggestions or surface relevant knowledge while you’re in a meeting, based on your calendar, meetings, knowledge base and web data. Users can also start a task on one device and continue on another.

Ambi co-founder Peter Mo.Image credits:ambi

“The way we interact with intelligence is limited. Often the intelligence is captured on the screen, and if you use hardware like Plaud, you have to wait for the recording to transfer to the phone, upload it to the cloud, and get the intelligence after processing.

The company has ambitions to capture more information. Its upcoming desktop app will be able to watch the work users are doing and store important parts in its memory to give more personalized suggestions. Apps like Littlebird or Minimi already do something similar.

The startup also plans to launch its own hardware device, which will have a screen, a speaker, a camera and microphones to capture context and display information.

Image credits:ambi

Question of privacy

Ambi’s launch comes amid growing concern about AI devices not having enough indicators to suggest they are capturing data about people around the user. It’s unclear whether Ambi’s apps or its future device will have enough indicators to alert third parties that their conversations are being recorded if the user doesn’t disclose their use of Ambi’s tech.

Some meeting note takers have even been sued because their tech was recording people’s conversations and data without their consent. Plus, the recent release of Apple’s Audio Intelligence feature, which continuously transcribes speech, has raised privacy concerns.

Mo said users can turn off ambient data capture on Ambi apps and devices at any time, and the feature is turned off by default. The startup said it does not store records of users and encrypts data, but it did not specify what kind of encryption it uses. Despite these measures, the onus to declare ambient capture is still on the user. He classifies this as an industry-wide problem.

“We need to inform our users that they need to tell others about ambient capture. We as an industry have not solved this problem [of disclosure]. We have tried to put the information about disclosure in every possible place, but if users try to hide the capture, we cannot solve the problem alone,” he said.

Ambi currently has over 50 employees across offices in San Francisco, Beijing, Shanghai and Shenzhen. The company is recruiting for its go-to-market function and AI engineers to work on speech recognition models.

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