Here’s a walkthrough: https://www.youtube.com/watch?v=TbOfx6UPuX4.

ML teams waste too much time on generic heavy lifting. Every project follows the same pattern: 20% understanding objectives, 60% wrangling data and engineering features, 20% experimenting with models. Most of this is formulaic but burns months of engineering time. Throwing LLMs at it isn't the answer as that just trades engineering time for compute costs and worse accuracy. Plexe automates this repetitive 80%, so your team can work faster on what actually has value.

You describe your problem in plain English ("fraud detection model for transactions" or "product embedding model for search"), connect your data (Postgres, Snowflake, S3, direct upload, etc), and then Plexe: - Analyzes data and engineers features automatically - Runs experiments across multiple architectures (logistic regression to neural nets) - Generates comprehensive evaluation reports with error analysis, robustness testing, and prioritized recommendations to provide actionable guidance - Deploys the best model with monitoring and automatic retraining

We did a Show HN for our open-source library five months ago (https://news.ycombinator.com/item?id=43906346). Since then, we've launched our commercial platform with interactive refinement, production-grade model evaluations, retraining pipeline, data connectors, analytics dashboards, and deployment for online and batch inference.

We use a multi-agent architecture where specialized agents handle different pipeline stages. Each agent focuses on its domain: data analysis, feature engineering, model selection, deployment, and so on. The platform tracks all experiments and generates exportable Python code.

Our open-source core (https://github.com/plexe-ai/plexe, Apache 2.0) remains free for local development. For the paid product, our pricing is usage-based, with a minimum top up of $10. Enterprises can self-host the entire platform. You can sign up on https://console.plexe.ai. Use promo code `LAUNCHDAY20` to get $20 to try out the platform.

We’d love to hear your thoughts on the problem and feedback on the platform!