You had the same conversation with your AI assistant three times this month. Not roughly the same—the same one. You explained what you're working on, who the stakeholders are, what you decided in last Tuesday's meeting. It helped, and then the next time you needed something, you explained it all over again.
Not because the AI forgot, exactly. Your previous conversations are probably still sitting in the sidebar. But the context from those conversations often doesn’t carry forward into the next one in any useful way. If you want the AI to know what you're working on, you have to tell it. Every time. The information exists somewhere, but the burden of assembling it, curating it, and delivering it to the right conversation at the right moment falls entirely on you.
So you copy from Slack, paste into the chat window, dig up the doc you sent last week, summarize the meeting that happened yesterday. You do ten minutes of context-setting to get something useful back. The output is usable, but only if you fill in the gaps yourself. You move on. But the friction is always there, and over time you stop noticing how much of your effort goes into bringing the AI up to speed instead of actually getting help.
This isn't just a personal frustration. It's a pattern. Early adoption of AI at work was fast because the tasks were simple and self-contained, like rewriting a paragraph or brainstorming campaign themes. But as people tried to use it for the work that actually matters, the context problem caught up with them. The tools kept getting smarter, but the time spent catching them up didn't shrink, it grew.
The obvious assumption is that the models need to get smarter. And they are getting smarter all the time. They write better, reason faster, and handle more complexity.
But that's not what’s broken.
Think about the difference between two coworkers. One just walked into the room. They're smart, they're paying attention, they can see exactly what's on your whiteboard. But they missed every conversation that led to what's on the whiteboard. They can help you with what's in front of you, but they'll suggest things you already tried, miss constraints you've already identified, and offer ideas that sound good in isolation but don't fit the decisions you've already made.
The other coworker has been in the room all week. They were in Monday's standup and heard you change your mind about the launch timeline on Wednesday. When they offer a suggestion, it lands because it accounts for everything that came before.
That second coworker is what AI at work should feel like.
And yet almost no AI works this way. Most AI tools are still designed around single conversations, not the accumulation of context over time. They optimize for what you can tell them right now, not what they should already know.
You don't write things down for Littlebird or feed it documents. You just work the way you normally do, and over days and weeks, it builds an understanding of your projects, your people, and your priorities.
It does this by passively reading the text in your active windows as you work. It transcribes your meetings without a bot joining the call. It sees your email when you're reading it, your Slack threads when you're in them, your docs when you're editing them. None of this requires setup or integrations. The context accumulates on its own.
That means the help you get on day 30 is meaningfully different from what you get on day one. You can ask it to draft a follow-up to Tuesday's client call and it already knows who was there, what was discussed, and what you committed to. The model didn't get smarter. It just learned more about your work, because it was paying attention while you were doing it.
If you've ever finished explaining something to an AI and thought, "I already told you this," that's exactly the feeling we’re building to eliminate.
