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.
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?
Timbal AI
@andrasczeizel Appreciate you saying that, means a lot given how much of the last two years went into exactly that side of it.
To your question: honestly, both, but for different reasons. Internal workflows tend to be the fastest wins, teams automating things like tender analysis, data cleanup, or ops tasks that used to eat a huge amount of manual hours. That's usually where clients start because the risk is lower and the ROI is immediate.
Customer-facing is where the governance and observability layer really earns its keep, once an agent is talking to your actual users, "it worked in testing" isn't good enough anymore. You need to know exactly why it made a decision, and you need guardrails that hold even when the input is messy. We've got clients running both today, but if I had to generalize, most start internal and move customer-facing once they trust the reliability layer.
Timbal AI
Hey Product Hunt 👋🏻
I'm Martí, co-founder and CEO of Timbal AI.
In today's AI world, going from 0 to 1 it's easy fast and cheap, going from 1 to 100 is not.
Complex data, legacy software, messy folders and heavy compliance and cybersecurity requirements, that's the enterprise reality.
You can prototype an agent in an afternoon, but then the real work starts. You have to wire together a vector database, an orchestration framework, a UI tool, and an observability layer. Before you know it, you're maintaining a fragmented stack of vendors, and your app still breaks in ways you can't easily trace.
Timbal is the unified stack that closes that gap. Everything you need to go from idea to production lives in one place:
A Database Native to AI: Run vector, keyword, and relational searches in a single query, ensuring your agents are always grounded in your actual data.
Deterministic Workflows: A reliable runtime for agents with built-in observability, traceability, and evals. You will always know exactly why your AI made a specific decision.
Omnichannel Visual Builder: Turn your logic into real apps instantly. Ship directly to the web, WhatsApp, email, or voice.
Our core is open source (Python framework, NPM packages, and TypeScript SDK on GitHub). You can stay in the code, build visually, or seamlessly mix both.
Putting this in front of the PH community is the milestone we’ve been waiting for. We want your brutal feedback—tell us what you'd build, what's missing, and where you think we're wrong.
Let's chat in the comments!
Martí & the Timbal team
Congrats on the launch! 🚀
Been using Timbal and honestly having all the tools I need in one place is what sold me, it makes building AI solutions genuinely simple instead of stitching together five different things. And the monitoring side is a huge plus, actually knowing what your agent is doing instead of guessing.
One question: does ACE ever get in the way when a task needs more flexible reasoning, or can you loosen it per agent/step?
Timbal AI
@carla_granados_soler Thank you so much for this, and for putting Timbal through its paces on the daily 🙌
Great question. ACE isn't one global switch, it's set per step/agent, so you can tighten it where you want strict guardrails (approvals, writes, anything customer facing) and loosen it where you actually want the model to explore, like open ended research or ambiguous classification.
The mechanism is the easy part honestly, the harder part is knowing where to draw that line for a given use case, we're still learning from real usage what the right defaults should be.
Have you hit a specific case where it felt too rigid? Would love to dig into it with you.
I enjoy platforms that remove unnecessary complexity. What level of customization do developers have for interfaces. More template examples could help new users build faster.
Timbal AI
@gaspard_dupuich Thanks Gaspard! Short answer: whatever level they want.
Think of it as a Lovable inside Timbal. You can keep iterating with Composer in natural language until the interface is exactly what you had in mind, and if you want to go deeper you can download the code or connect your GitHub and work on it directly. It's real code you own, not a locked template, so there's no ceiling where the builder stops and you're stuck.
On templates: we already ship with them. They're not exposed as a gallery in the platform yet (coming soon), but Composer already fetches them from the backend and picks the ones that match the intent of your prompt. So in practice you're rarely starting from a blank canvas, even if you never see the template library itself.
Curious what kind of interface you'd build first, that helps us prioritize which templates to surface.
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.
Timbal AI
@anthonywrinqsb Yeah, this is one we see constantly. The AI part rarely breaks first, it's the coordination layer around it. One person tweaks a prompt, someone else changes the data schema, and two days later nobody can explain why the agent's behavior shifted.
We actually live this ourselves internally, we're selective about the AI projects we take on, each one led by a small, focused team, but we help each other across projects constantly. Having workflows and traces live as an actual git repo (commits, diffs, branches) helps a lot here, everyone sees exactly what changed and when, instead of "it worked yesterday" being the only explanation you've got.
how do you manage version control across workflows. a simple visual history could help teams track every important change.
Timbal AI
@darly_selby Great question! Every workflow in Timbal is a git repo under the hood full commits, diffs, branches, the works. You can clone it, review it on GitHub, and roll back exactly like any other codebase. Environments map to branches too, so promoting dev → prod is a merge, not a copy-paste.
Love the visual history idea though!
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.
Timbal AI
@georgiafor9p Georgia, that's the whole bet we're making. Every "just add one more tool" decision feels small in the moment, but a year in, most teams are maintaining a stack nobody fully understands anymore, including the people who built it.
Timbal replaces the orchestration layer, the data layer, the UI layer, and observability in one runtime. Not five vendors talking to each other through glue code, one system that already knows about all the pieces.
Timbal AI
@georgiafor9p Indeed! And this is one of the main bets we are making. Most teams end up with a vector DB, an orchestration framework, a UI tool, and an observability layer, all coming from separate vendors and having their own quirks, so it involves a lot of work to glue them together instead of building the actual product.
With Timbal, knowledge bases and retrieval run on our own hybrid DB engine instead of a separate vector DB, the UI builder is native, and observability and evaluations are part of the runtime instead of a dashboard. This means that Timbal allows you to have the four or five tools you'd normally stitch together but already in the same place and seamlessly talking to each other.