Three former Google X scientists aim to give you a second brain virtually. This is not in the sci-fi or chip-in-your-head sense, but through an AI-powered app that gains context by listening to everything you say in the background. Their startup, TwinMind, has raised $5.7 million in seed funding and released an Android version, along with a new AI speech model. It also has an iPhone version.
Co-founded in March 2024 by Daniel George and his former Google X colleagues Sunny Tang and Mahi Karim, TwinMind runs in the background, capturing ambient speech with user permission to build a personal knowledge graph. By turning spoken thoughts, meetings, lectures, and conversations into structured memory, the app can generate AI-powered notes, to-dos, and answers.
It works offline, processes audio in real-time to transcribe on-device, and can capture audio continuously for 16 to 17 hours without draining the device’s battery, according to the founders. The app can back up user data so conversations can be recovered if the device is lost, though users can opt-out of that. It also supports real-time translation in over 100 languages.
TwinMind differentiates itself from AI meeting note-takers by capturing audio passively in the background all day. To make this possible, the team built a low-level service in pure Swift that runs natively on the iPhone. In contrast, many competitors use React Native and rely on cloud-based processing, which Apple restricts from running in the background for extended periods.
The co-founders spent about six to seven months perfecting the continuous audio capture and finding ways to work within Apple’s restrictions. George left Google X in 2020 and got the idea for TwinMind in 2023 while working at JPMorgan as Vice President and Applied AI Lead, attending back-to-back meetings each day. To save time, he built a script that captured audio, transcribed it on his iPad, and fed it into ChatGPT, which began to understand his projects and generate usable code. After sharing the results with friends and on a forum, he was inspired to build an app that could run on a personal phone.
In addition to the mobile app, TwinMind offers a Chrome extension that gathers additional context through browser activity. Using vision AI, it can visually scan open tabs and interpret content from various platforms, including email, Slack, and Notion. The startup even used the extension itself to shortlist interns from over 850 applications they received this summer by opening all the LinkedIn profiles and CVs in browser tabs and asking the extension to rank the best candidates.
Current AI chatbots cannot easily process hundreds of documents or parse sign-ups from tools like LinkedIn or Gmail to gather contextual information. Similarly, AI-powered browsers lack the ability to build knowledge from your offline conversations and in-person meetings.
The startup currently has over 30,000 users, with about 15,000 of them active each month. As much as 20 to 30 percent of TwinMind users also use the Chrome extension. While the U.S. is the largest base for TwinMind so far, the startup is also seeing traction from India, Brazil, the Philippines, Ethiopia, Kenya, and Europe.
TwinMind targets a general audience, although 50 to 60 percent of its users are currently professionals, about 25 percent are students, and the remaining 20 to 25 percent are individuals using it for personal purposes. George noted that his father is among the individuals using TwinMind to write an autobiography.
One of AI’s significant drawbacks is its potential to compromise user privacy. But George asserted that TwinMind does not train its models on user data and is designed to work without sending recordings to the cloud. Unlike many other AI note-taking apps, TwinMind does not let users access audio recordings later. The audio is deleted immediately, while only the transcribed text is stored locally in the app.
The TwinMind co-founders spent a few years working on various projects at Google X. That experience helped the team move quickly from concept to product. George stated that Google X was the perfect place to prepare for starting a company, as it offered the experience of working on multiple early-stage startup-like projects in a short span.
Before joining Google, George worked on applying deep learning to gravitational wave astrophysics as part of the Nobel Prize-winning LIGO group. He completed his PhD in AI for astrophysics in just one year at the age of 24, a feat that led him to join Stephen Wolfram’s research lab in 2017 as a deep learning and AI researcher. That early connection with Wolfram came full circle years later, as Wolfram wrote the first check for TwinMind, marking his first-ever investment in a startup. The recent seed round was led by Streamlined Ventures, with participation from Sequoia Capital and other investors, including Wolfram. The round values TwinMind at $60 million post-money.
In addition to its apps and browser extension, TwinMind has introduced the TwinMind Ear-3 model, a successor to its existing Ear-2. The new model supports over 140 languages worldwide and has a word error rate of 5.26 percent. It can also recognize different speakers in a conversation and has a speaker diarization error rate of 3.8 percent.
The new AI model is a fine-tuned blend of several open-source models, trained on a curated set of human-annotated internet data, including podcasts, videos, and movies. The company found that supporting more languages helps the model get better at understanding accents and regional dialects because it trains on a broader range of speakers.
The model costs $0.23 per hour and will be available through an API to developers and enterprises over the next few weeks. Unlike the Ear-2, the Ear-3 does not support a complete offline experience, as it is larger in size and runs on the cloud. However, the app automatically switches to Ear-2 if the internet connection is lost and moves back to Ear-3 when it is restored.
With the introduction of the Ear-3, TwinMind now offers a Pro subscription at $15 per month, which provides a larger context window of up to 2 million tokens and email support within 24 hours. The free version still exists with all the existing features, including unlimited hours of transcriptions and on-device speech recognition.
The startup currently has a team of 11 members. It plans to hire a few designers to enhance its user experience and set up a business development team to sell its API. Furthermore, there are plans to spend some money on acquiring new users.