Timbal AI

Build AI agents, workflows, and apps in one stack

Productivity

SaaS

Artificial Intelligence

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The tool-stitching problem is so real. Spent way too long gluing together separate tools for orchestration, logging, and UI. Makes total sense to have one platform for all of it. Congrats on the launch 🎉

How does pricing scale as you add more agents and team members, especially once you start hitting heavier eval runs on bigger workloads?

Congrats on the launch! I build AI agents internally for a 40-person company and my current stack is duct tape: prompts in one place, tools in another, deployment somewhere else. One stack for all of it is exactly the pitch that gets me. Question: how do you handle testing before an agent hits production? Rolling back a bad prompt change has burned me more than once, so versioning and evals are what I’d look at first.

the trace-at-every-step answer to Shubham's question is the part that sold me, that's the actual difference between a demo and something a team will trust in production. one thing I'd want to understand before committing though: once retrieval, orchestration, UI, observability and evals all live in one core, how painful is it to rip out just one piece later if a team outgrows it or needs something more specialized, or is the whole pitch that you shouldn't need to

How does Timbal handle versioning when you iterate on agents and workflows, and can you roll back to a previous version if something breaks in production?

Love how you're baking governance and step-level tracing into the runtime itself, turning the usual after-the-fact scramble into something you can just replay and inspect.

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?

@pedrolivares — the part that resonates: making the resilience logic — per-step retries, primary→secondary model fallback, human-in-the-loop — a property of the runtime instead of glue code every team rewrites and half-tests.

I've watched agent projects die right in that "between the tools" seam, so having ACE enforce expected behavior at each node and trace every retry/fallback is the piece I'd actually trust in production.

Model-agnostic with the fallbacks baked in is the right call too.

Congrats on the launch!

I spend most of my day talking to enterprise teams who are stuck exactly where you're describing: impressive demo, then six months of stitching together retrieval, observability, and governance before legal will even let it near production. The human in the loop piece is what caught my eye, since "who approved this agent's decision" is usually the question that kills deals late in procurement.

Curious how long your average enterprise sales cycle is now that you've built compliance in from the start, has it actually gotten shorter?

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.