Launching Impulse AI: An Autonomous ML Engineer That Placed Top 2.5% on Kaggle

Today we're launching Impulse AI—an autonomous machine learning engineer that turns your data into production-ready models from a simple prompt. Upload your customer data, tell Impulse "predict which customers will churn in the next 90 days," and get a deployed model with monitoring, retraining pipelines, and audit logs. No code required. No ML expertise needed. The entire ML workflow—from messy data to production deployment—is automated and accessible at impulselabs.ai starting today.

Most companies face the same ML bottleneck: non-technical teams can't build models because they lack ML resources, and technical teams can't move fast enough because they're overwhelmed with demand. Product managers have customer data that could predict churn but no capacity to build the model—they'd need to wait months for engineering resources or hire expensive help they can't afford. For companies without ML engineers, the options are limited: hire a $200K+ engineer (if they can find one), pay consultants $50K+ for months-long projects, or give up and rely on spreadsheets. For companies with ML teams, the bottleneck is just as real—they have more product work than they can handle, backlog requests piling up from every department, and no budget to hire more people. The result is billions in trapped business value because building even simple ML models is either impossibly expensive for non-technical teams or impossibly slow for technical teams drowning in requests.

We proved our autonomous agent works by entering it into a featured Kaggle competition where ML engineers compete to build the best predictive models. The challenge: predict loan payback behavior from financial data. We submitted our agent's work with zero human intervention—no manual feature engineering, no hyperparameter tuning, just autonomous operation. Result: rank 782 out of 31,791 participants, top 2.5%. Our agent beat 97.5% of human ML engineers, including teams from top tech companies and PhD researchers. 

How It Works

1. Intent Understanding – Describe your goal in plain English and the agent understands business context, not just technical specifications.

2. Automated Data Preparation – The agent handles missing values, detects quality issues, and makes intelligent cleaning decisions.

3. Feature Engineering – Features are created automatically based on your task, understanding temporal relationships and interactions.

4. Model Selection – The agent evaluates different models and selects the best architecture for your data and use case.

5. Safety Checks – Comprehensive validation prevents data leakage and ensures production reliability.

6. Deployment – Automatic deployment with drift detection, retraining, audit logs, and approval workflows.

7. Self-Improvement – The agent learns from production feedback, improving with each model.

Try for free at: https://app.impulselabs.ai/ 

About Impulse AI

Impulse AI is building an autonomous machine learning engineer that turns data into production models from a simple prompt. Founded in 2025 and based in California, the company enables teams to build, deploy, and monitor expert-level ML models without code or specialized ML expertise. For more information, visit https://www.impulselabs.ai.