Timbal AI

Honestly the part that gets me is that gap after the first demo, thats always where my projects start falling apart lol. so seeing you focus on the production side and not just the shiny prototype is refreshing.

quick q on the data side, how do the knowledge bases work? can i connect my own KBs through MCP or does everything have to go through timbals own ingestion? curious how that plays with retrieval before i try moving stuff over.

I like that you’re combining orchestration, deployment, observability, and evaluation instead of expecting teams to stitch together several different tools. I’m curious how opinionated the evaluation layer is—can teams bring their own eval datasets and metrics, or does Timbal encourage a particular workflow?

"prototypes into production systems" is exactly the right problem to focus on. the graveyard of AI demos that never made it to real users is huge and the gap is almost never the model quality. it's observability, governance, eval pipelines, and the ten other boring things that prototype tools don't include. curious how the governance layer works in practice though. when an agent does something unexpected in production, how quickly can you trace back through the decision chain to understand why it happened?

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.

I appreciate that you're trying to simplify the AI development process without hiding the important pieces. reliability and visibility become much more valuable as projects start to grow.

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.

i like products that solve workflow issue instead of adding another tool to the stack. if Timbal can replace a few separate services that 's already a big win in my book.

i've noticed that AI projects become difficult to manage as soon as more people join team. having everything in one place could make collaboration a lot smoother.

@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.

The prototype-to-production gap for AI apps is very real. It is easy to get something impressive working in a demo, but then retrieval, orchestration, ui, monitoring, evals, permissions, and governance all become separate problems very quickly. as someone building a product, I can definitely see the appeal of having one place to move from "this agent works locally" to "this is actually reliable enough for users."Curious where Timbal is strongest today. is it mainly for teams building internal AI workflows, or are people also using it for customer-facing AI products?