Launching today
Timbal helps teams turn AI prototypes into production systems. Build agents and workflows, connect them to your data, design interfaces, deploy, monitor, evaluate, and govern everything from one platform. Instead of assembling separate tools for retrieval, orchestration, UI, observability, and evals, Timbal gives you one core for shipping reliable AI applications.
Timbal AI
Hey PH! Pedro here, Head of Product at Timbal.
Martí covered the big picture, so I wanted to add a bit on Composer specifically, the piece I spent the most time on for this launch. Most no-code builders get you 80% of the way and then you hit a wall the moment you need real logic, a tricky auth flow, or a data model that doesn't fit the template. Composer lets you drop into actual code right at that point, no rewrite, no migration to a "real" stack later.
If you've ever hit that wall with another builder, I'd love to hear what broke it for you. That's exactly the kind of feedback that shapes what we build next.
Thanks for checking us out today 🙌
Agent 37
i find myself switching between too many AI tools during a signle project. having one platform for the whole workflow sounds much more manageable . less time configuring tools usually means more time improving the product itself.
Timbal AI
@amanda_silmon That's the exact reason this whole thing exists, Amanda!
We got tired of watching people spend more time configuring tools than actually building the thing they cared about.
Since you're here and clearly get it, we're giving away 40,000 free credits right now for anyone who wants to put Timbal through a real project. Would love to see what you'd build with it 🙌
Timbal AI
@amanda_silmon @inescastillo Thanks for the support, Amanda! I used to have the same issue before Timbal, it saves a lot of time and makes everything much more efficient :)
@marti_norberto I appreciate that this isn't just another agent builder. It feels like you're trying to solve the production side of AI as well. That's the part I usually end up spending the most time on.
Timbal AI
@marti_norberto @aduma__emmanuel That's the part that took us the longest to get right, and honestly the part most builders underestimate until they're already in production and things start breaking in ways they can't trace.
Getting an agent to work is the easy 10%. The other 90% is retrieval, observability, evals, permissions, all the stuff that never shows up in a demo but decides whether you can actually trust the thing with real users. That's the whole reason Timbal exists.
Really like the positioning around helping teams move from AI prototypes to production—it feels like a problem a lot of builders eventually run into. I'm curious, after working with customers, what's the most common reason promising AI prototypes never make it to production? Is it usually reliability, observability, governance, or something else that catches teams by surprise?
Hello Pedro, building agents is getting easier every day, but deploying and maintaining them is still a challenge. Nice to see a platform tackling the whole lifecycle.
Timbal AI
@mathew_chang Thank you your support, Mathew! We are very proud that we manage to tackle the whole lifecyle, we believe it is a big step forward!
I've noticed that every new AI projects seems to introduce another tool into the stack. If Timble can replace even a few of those I can see value straight away.
Timbal AI
@edith__christian Edith, exactly the pattern we kept running into with our own clients before building this.
Every new AI initiative meant another subscription, another auth flow, another thing to monitor separately.
Happy to get specific if it helps. What's currently in your stack that you'd want to see replaced first? That tells us a lot about where the pain is actually concentrated.