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Home » Littlebird raises $11 million for AI-assisted ‘recall’ tool to read computer screens
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Littlebird raises $11 million for AI-assisted ‘recall’ tool to read computer screens

adminBy adminMarch 23, 2026No Comments6 Mins Read
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There has been a lot of discussion about building context for AI systems. In consumer software, we’ve seen startups built around search, documents, and conferencing. They all want to capture context from your digital life, provide connectivity to other tools, and allow you to query all your data. Some tools have gone even further. For example, Rewind (which became Limitless and was sold to Meta) and Microsoft Recall aim to capture everything that’s happening on your screen and help you remember it all.

A new startup called Littlebird is trying to do something similar with a slightly different approach. While apps like Rewind save screenshots or some kind of visual data, Littlebird “reads” the screen and saves the context in text format.

The core idea behind this product is that since the product is always reading the screen, there is no need to provide additional context for productivity. The startup believes that while many AI tools try to distract users, Littlebird can work in the background and only appear when users want it.

Image credit: Little Bird

When you set up Littlebird on your computer, you can customize which apps to ignore and not capture context. The startup said it automatically ignores sensitive fields in password managers and web forms, such as passwords and credit card details. You can also connect other apps like Gmail, Google Calendar, Apple Calendar, and Reminders to this app.

The app allows you to ask questions about your data and provides pre-generated prompts to get you started, such as “What did you do today?” “What kind of emails are important to me?” After using it for a few days, I noticed that these prompts became more personalized over time.

Littlebird also has a built-in notetaker, like Granola, that runs in the background using system audio, captures transcriptions of meetings, and creates notes and action items based on them. When you open a meeting in details view, there is an option called “Prepare Meeting” that displays the meeting details considering the context of past meetings, emails, and company history. This feature pulls information from sources like Reddit to tell you what users think about a particular product or company.

Image credit: Little Bird

Another tool called Routines provides detailed prompts for running Littlebird at recurring intervals, such as daily, weekly, or monthly. The company lists several ready-to-use routines, including a daily briefing, a weekly activity summary, and a summary of yesterday’s work. Users can also create their own routines using custom instructions.

Littlebird was founded in 2024 by Arup Shah, Naman Shah, and Alexander Green. Arup and the Naman brothers founded Sentieo, an institutional investor platform, which was sold to market intelligence firm AlphaSense. They previously co-founded a health food company called Thistle. Arup was also a co-author of the much-talked-about Citrini paper about how AI agents could disrupt the economy, causing various tech stocks to fall as a result. Green has founded various companies in the areas of hardware, software, and AI.

“We started when Alap raised an interesting problem about AI becoming about your (user) data. The model doesn’t know anything about you, which limits its usefulness. We were thinking about different UI and OS paradigms that were likely ripe for disruption by AI, and that started Littlebird as a project,” Green told TechCrunch by phone.

Green noted that while Rewind was close to what Littlebird was trying to do, it relied on screenshots and didn’t provide a great search experience. He said the startup is still in its infancy and there are still many problems to solve, such as making large-scale language models (LLMs) understand different kinds of contexts about users.

With Littlebird, users can delete their data at any time, and the data is encrypted and stored in the cloud. Green said the rationale for storing data in the cloud is to run powerful models that support a variety of AI workflows that cannot be run locally.

“It doesn’t store any visual information. It only stores text, so it’s much lighter data. I think that’s probably another reason why Recall and Rewind struggled, and that’s that taking a screenshot consumes more data. I also think it’s more invasive,” he said.

Image credit: Alexander Green

Littlebird is free to download and use, but for increased usage limits and access to features such as image generation, users can pay for plans starting at $20 per month.

The startup was led by Lotus Studio, with participation from Lenny Rachitsky, Scott Belsky, Gokul Rajaram, Justin Rosenstein, Shawn Wang, and Russ Heddleston, and has raised $11 million in funding.

Some of these investors are regular users of the product. Mr. Rajaram, who has worked on ad products at Google and Facebook, said the product eliminates the hassle of remembering, searching, and restating your work. Heddleston, co-founder and CEO of DocSend, said he used the tool to rewrite his company’s marketing site, taking advantage of contexts such as meetings, email, and Notion.

Rachitzky, who runs his own newsletter and podcast, said AI is only as good as the context it has and that it misses out on a lot of everyday life. He said he asks tools about improving his productivity workflow and being happier. He said that for long-term success, products need to find killer use cases.

“I think the key is finding the use cases that are absolutely necessary. That’s all that’s important to the success of this product at this point. I know a lot of people have already found that themselves. The team is leaning into these experiences as they see these use cases emerging,” he said.

“I’ve been on a lot of AI product builders on the podcast, and the most consistent theme is that you don’t know how people will actually use your product until you put it out there. The strategy is to get something early out there, see how people use it, and then double down on those use cases instead of waiting for something fully understood.”



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