Launching today
Impulse AI
From plain English to deployed ML model in an hour
27 followers
From plain English to deployed ML model in an hour
27 followers
Impulse is an autonomous ML engineer. Describe a prediction problem in plain English, connect your data, get a deployed model with an API in under an hour. Our agent placed top 2.5% on a Kaggle competition. 782 of 31,791, with zero human intervention. No prompt engineering, no tuning. AutoML tools need configuration. No-code builders stop at a trained model. Impulse handles the full workflow: cleaning, feature engineering, model selection, leakage checks, deployment, and monitoring.
Hey Product Hunt 👋 Derek here, an engineer at Impulse AI
Over the years I’ve built distributed systems, developer platforms, runtimes, observability stacks, blockchain infrastructure, and more recently large scale agentic AI systems, and one thing keeps repeating itself: most companies are not actually blocked by lack of data or even lack of models, they’re blocked by the gap between having an idea and turning it into something operational that can predict outcomes and actually run in production.
Most prediction projects never leave spreadsheets, dashboards, or “we should build this someday” conversations because getting from raw data to a deployed model still takes coordination across engineering, ML, infrastructure, ops, compliance, and business teams, and honestly that process is still way too heavy.
So we built Impulse AI to make that workflow feel dramatically simpler. You describe what you want to predict in plain English, connect your data, and the platform handles the rest: cleanup, feature engineering, evaluation, leakage detection, model selection, training, deployment, monitoring, and inference APIs, all through an agentic workflow designed to behave more like an ML engineer than a static AutoML tool.
One thing we care deeply about is making AI systems feel operational instead of experimental. Not just “here’s a model,” but something observable, traceable, deployable, and capable of continuously improving over time. Every step in the loop is measured, evaluated, and inspectable because once AI becomes a black box, trust disappears fast.
We’re not trying to replace ML engineers. Great ML engineers are still incredibly valuable. The goal is enabling the people closest to the business problem to move at the speed of thought instead of waiting months for cross-team coordination just to test an idea.
Would genuinely love feedback:
What’s the first prediction workflow inside your company you’d automate if the barrier disappeared?
Where do you think agentic ML systems still break down today: trust, observability, deployment, correctness, cost, or something else entirely?
If this worked exactly as advertised, what part of your current workflow would it replace first?
Would love the hard critiques, skepticism, weird use cases, and edge cases. Those conversations usually end up shaping the roadmap the most.
Hey Product Hunt 👋 JP here, a research engineer at Impulse AI. I am the one in charge of making the whole system smarter.
When we were building Impulse, we wanted to empower technical and non-technical folks alike. Our core business proposition is fairly straightforward: we allow our users to go from "data + question in plain English" to "deployed model(s) + insights & oracles, together with a deployed API" in a matter of minutes. In our view, this can significantly accelerate the way data-driven people work. This is what we're currently doing, and we are launching with a fun (but tough!) example: predicting the NBA playoffs.
Would greatly appreciate your feedback on:
Product clarity, UX, user flow.
Reliability and utility of the deployed models, suggestions, etc.
Suggestions or use cases you'd like us to demo (e.g., predict remaining battery life for an electric vehicle)
Feature requests, ideas, criticisms,
Anything else you can think of.
Thank you for testing and for helping us improve our product!
Super intriguing! I've wanted this kind of customization for a while but I assumed it would be too expensive to get going. Just signed up.
@codybrown let me know what you think! happy to walk you through the product if you have questions!
Huddle01 VMs
@ranjan3118 thank you! appreciate the feedback that you made on our previous version. would love for you to try the new version!