Startup Littlebird has raised $11 million in a new funding round to develop an AI-powered “recall” tool that captures context directly from a user’s computer, enabling them to query their own digital activity seamlessly. The round was led by Lotus Studio, with participation from notable investors including Lenny Rachitsky, Scott Belsky, Gokul Rajaram, Justin Rosenstein, and Russ Heddleston.
Founded in 2024 by Alap Shah, Naman Shah, and Alexander Green, the company is building an AI system that continuously reads on-screen content in real time and converts it into structured text. Unlike similar tools that rely on screenshots, Littlebird focuses on extracting contextual information, making it lighter, more searchable, and potentially less intrusive.
The core idea behind the product is to eliminate the need for users to repeatedly provide context to AI tools. By passively understanding emails, documents, meetings, and browsing activity, the system allows users to ask questions such as “What have I been working on today?” or “Which emails are most important?” with responses becoming more personalized over time.
Littlebird also includes features such as a background meeting assistant that transcribes audio and generates notes, as well as “Routines” that can automate recurring insights like daily briefings or weekly summaries. The platform integrates with tools like Gmail and calendars while automatically excluding sensitive data such as passwords and financial inputs.
Explaining the vision, co-founder Alexander Green said, “Models don’t know anything about you, and that limits their utility,” highlighting the importance of personal context in making AI more useful.
The company stores user data in encrypted cloud systems to enable more powerful AI processing, while allowing users to control what is captured and delete their data when needed. Littlebird follows a freemium model, with paid plans starting at $20 per month for advanced features.
As competition grows in the AI productivity space, Littlebird’s approach reflects a broader shift toward context-aware systems that aim to move beyond generic responses and deliver highly personalized, memory-driven assistance across everyday workflows.
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