There has been extensive discussion about establishing context for AI systems. In the realm of consumer software, startups have emerged focusing on search, documents, and meetings. Their aim is to capture context from your digital activities, create links to additional tools, and allow you to query that information. Some tools have advanced further. For example, Rewind (which transitioned to Limitless and was acquired by Meta) and Microsoft Recall seek to log everything occurring on your screen and assist you in recalling it all.

A fresh startup named Littlebird is pursuing a comparable goal but with a slightly different method. Unlike applications like Rewind that preserve screenshots or some form of visual data, Littlebird is “reading” the screen and logging the context in a text format.

The fundamental concept driving the product is that, by continuously reading your screen, you do not need to provide extra context for productivity. The startup asserts that while many AI tools aim to divert your attention, Littlebird operates quietly in the background, surfacing only when you desire it to.

Upon installing Littlebird on your device, you can specify which applications you want it to overlook and avoid capturing context. The startup claims that it automatically disregards password managers and sensitive input fields in internet forms, such as passwords and credit card information. You can also link additional applications like Gmail, Google Calendar, Apple Calendar, and Reminders to the app.

The application allows you to inquire about your data, providing pre-made prompts to initiate your queries, such as “What have I accomplished today?” or “Which emails are significant to me?” Within a few days of utilizing the app, I found that these prompts became increasingly tailored as time progressed.

Littlebird features an integrated notetaker similar to Granola that utilizes system audio and operates in the background to transcribe meetings and generate notes and action items. When viewing a meeting in detail, there’s an option labeled “Prep for meeting” that considers previous meetings, emails, and company history to furnish you with further insights about the upcoming meeting. This feature also gathers information from platforms like Reddit to inform you about public sentiment regarding a specific product or company.

Another feature named Routines provides detailed prompts for Littlebird to execute at specified intervals, including daily, weekly, or monthly. The company offers several pre-configured routines like daily briefings, weekly activity summaries, and summaries of previous day’s work. Users are also able to design their own routines with tailored instructions.

Littlebird was established by Alap Shah, Naman Shah, and Alexander Green in 2024. Siblings Alap and Naman previously launched Sentieo, a platform aimed at institutional investors, which was sold to the market intelligence company AlphaSense. They also co-founded a wellness food venture called Thistle. Alap was a co-author of the widely discussed Citrini paper on how AI agents could lead to economic disruption, resulting in declines in various tech stocks. Green has founded several enterprises in the fields of hardware, software, and AI.

“We initiated this project when Alap brought up a compelling issue that AI will revolve around the data of [users]. Models lack knowledge about users, limiting their effectiveness. We contemplated different UI and OS paradigms ripe for disruption by AI, and that sparked Littlebird as a project,” Green shared with TechCrunch during a phone call.

Green remarked that while Rewind was somewhat aligned with Littlebird’s goals, it relied heavily on screenshots and did not provide an optimal search experience. He noted that the startup is merely at the beginning of its journey and has a multitude of issues to address, including enabling large language models (LLMs) to grasp various types of user context.

With Littlebird, users have the ability to delete their data at any moment, and their information is stored in the cloud with encryption. Green mentioned that the decision to store data in the cloud was made to facilitate powerful models for various AI processes, which cannot be achieved locally.

“We do not retain any visual data. We solely keep text, making the data significantly lighter. This was likely a factor that contributed to the struggles of Recall and Rewind, as capturing a screenshot requires substantially more data. Furthermore, I believe it is also more intrusive,” he explained.

Littlebird is available for free download and use, but to access enhanced usage limits and features like image generation, users have the option of paid plans starting at $20 monthly.

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

Many of these investors are active users of the product. Rajaram, who has experience at Google and Facebook within advertising, stated that the product alleviates the difficulty of recalling, accessing, and re-explaining one’s own work. DocSend co-founder and CEO Heddleston shared that he revised the firm’s marketing website using the tool, leveraging context from meetings, emails, Notion, and more.

Rachitsky, who manages his own newsletter and podcast, remarked that AI’s effectiveness is linked to the context it possesses, acknowledging that it frequently overlooks significant elements of your day. He mentioned that he utilizes the tool to enhance his productivity strategies and to foster greater happiness. For continued success, he suggested that the product would need to discover a major use case.

“I believe it is crucial to identify that essential use case. That is what determines this product’s current success. Numerous individuals have already identified that for themselves, and the team is attuned to these experiences as they recognize these use cases developing,” he observed.

“I’ve hosted numerous AI product creators on the podcast, and a recurring theme is that you won’t truly understand how users will interact with your product until it is released. The strategy is to launch initial versions, observe user interactions, and focus on those use cases instead of waiting for everything to be perfectly defined.”