Trilo is designed to simplify and streamline the way individuals and teams manage their work. In today’s fast-paced environment, juggling multiple tasks, conversations, and projects across different tools can be overwhelming. Most productivity tools focus on organizing information, but they don’t actually help you execute work. Trilo addresses this gap by turning communication directly into action, allowing users to focus on what matters instead of constantly managing their workflow.
We’re still early, but one thing that stands out already is how much time is saved on repetitive actions—creating tasks, summarizing conversations, tracking updates.
As a small team, we felt the pain of coordination more than anything else.
Trilo is built around reducing that—less manual tracking, fewer missed tasks, and better alignment across conversations and work.
One use case we’re excited about is meeting workflows.
Instead of writing notes manually, Trilo summarizes discussions and pulls out action items, which can then be tracked as tasks automatically.
We’re not trying to replace every tool with Trilo, but rather reduce the need to constantly switch between them.
By connecting chat, tasks, docs, and calendar, and layering AI on top, we’re aiming for a smoother workflow overall.
For us, the biggest problem wasn’t task management—it was everything around it.
Conversations, updates, follow-ups… all of that takes time.
Trilo is basically our attempt to reduce “workflow friction.”
Instead of jumping between tools, everything lives in one place, and the AI helps turn discussions into actions.
It’s especially useful for async teams where things can easily get lost in chats.
One interesting challenge we’re working on with Trilo is context—how to make sure the AI understands ongoing conversations and turns them into meaningful tasks.
Right now it helps with task extraction, summaries, and keeping workflows connected, but there’s a lot more to build here.
While building Trilo, we focused a lot on the idea of “execution” instead of just organization.
For example, instead of manually creating tasks after a conversation, the system does it for you. Same with meeting summaries and follow-ups.
Still improving how the AI handles complex scenarios, but even basic automation saves time.
Not sure yet how reliable AI can be for full task execution, but definitely an interesting direction.
One thing I’m curious about—how well does Trilo handle messy conversations where tasks aren’t clearly defined?
I like the idea of reducing tool switching. That's honestly one of the biggest productivity drains for me.
The '' execution vs organization'' point really stands out here. Most tools stop at tracking, not doing. Curious how far automation can go in real workflows.
I'm still not sure how reliable the AI is, but certainly an interesting aspect. 🤔
I like the idea of automatically turning chats into tasks. It's something I struggle with everyday. @harold_moore
Been trying a few AI productivity tools lately, but most feel like “assistants.” This is interesting because it’s trying to actually do the work. Curious how it handles edge cases though.
We’re still early, but one thing that stands out already is how much time is saved on repetitive actions—creating tasks, summarizing conversations, tracking updates.
Even partial automation makes a difference.