Littlebird secures $11M for AI screen-reading recall tool

Capturing and utilizing personal context is a central challenge for modern AI tools. While many applications focus on specific domains like search or documents, a new wave of software aims to build a comprehensive, searchable memory of your entire digital activity. Following the path of tools like Rewind and Microsoft Recall, a startup named Littlebird has entered this space with a distinct technical approach and has secured $11 million in funding to develop its vision.

Littlebird’s fundamental innovation lies in how it captures information. Instead of storing screenshots or visual data, the software actively reads on-screen content and archives it as text. This method, according to the founders, creates a lighter-weight, less invasive, and more searchable record of a user’s digital context. The product operates continuously in the background, designed to surface only when summoned, thereby minimizing distraction while building a rich, queryable history of a user’s work.

User control and privacy are built into the system. During setup, individuals can select which applications to ignore, and the tool automatically avoids sensitive fields like passwords and credit card details. It also integrates with common productivity platforms such as Gmail, Google Calendar, and Apple Reminders. From this aggregated data, users can ask natural language questions, with the app offering personalized prompts over time, like “What have I been doing today?”

The feature set extends beyond simple recall. An integrated AI notetaker uses system audio to transcribe meetings and generate notes and action items. A “Prep for meeting” function analyzes context from past correspondence and meetings, and can even pull external data from sources like Reddit to provide background on companies or products. Another component, called Routines, allows for automated, periodic reports such as daily briefings or weekly summaries, which can be customized with user-defined instructions.

Littlebird was founded in 2024 by Alap Shah, Naman Shah, and Alexander Green. The Shah brothers previously founded and sold the institutional investor platform Sentieo to AlphaSense. Alexander Green brings experience from building companies across hardware, software, and AI. The founding insight, as Green explained, was that AI models lack personal context, which limits their utility. The team saw an opportunity to reimagine user interfaces and operating systems around this personalized data layer.

Green acknowledges predecessors like Rewind but argues their reliance on screenshots created a poor search experience and significant data overhead. “We don’t store any visual information. We only store text,” he stated, suggesting this makes the platform less data-hungry and invasive. All user data is encrypted and stored in the cloud, a design choice that enables the use of powerful AI models for various workflows, though users can delete their data at any time.

The startup operates on a freemium model. The core app is free to download, with paid plans starting at $20 per month unlocking higher usage limits and advanced features like image generation. The recent $11 million funding round was led by Lotus Studio and included a roster of angel investors like Lenny Rachitsky, Scott Belsky, and Gokul Rajaram, many of whom are active users.

These investors highlight the product’s potential to reduce cognitive friction. Rajaram noted it eliminates the burden of “remembering, retrieving, and re-explaining your own work.” DocSend co-founder Russ Heddleston used Littlebird’s aggregated context from emails and meetings to rewrite his company’s marketing site. For long-term success, however, investors like Rachitsky believe the key is identifying a killer use case. He observes that the most successful AI products often discover their essential utility only after being released to users, a strategy Littlebird is now pursuing by observing how early adopters integrate the tool into their workflows.