HeyNEO provides an autonomous AI engineer called NEO that automates the entire machine‑learning workflow—from data cleaning and feature engineering to model training, hyperparameter tuning, RAG pipeline creation, and production deployment—through a chat‑based interface. By leveraging parallel agents and multi‑step reasoning, NEO reduces manual effort, accelerates experimentation, and delivers production‑ready models while integrating with users' own compute environments.
Funding
Funding not disclosed
Founders
Product
Problem
Machine learning engineers spend thousands of hours on repetitive tasks such as data preprocessing, model training, hyperparameter tuning, and deployment pipeline setup, which slows experimentation and delays product delivery.
Solution
HeyNEO offers an autonomous AI engineer named NEO that automates the full ML workflow through a network of parallel agents. Users interact with NEO via chat to define objectives, and the system conducts data analysis, builds and fine‑tunes models, creates retrieval‑augmented generation pipelines, runs evaluations, and prepares production‑ready deployments. NEO employs multi‑step reasoning to explore alternative approaches, assess risks, and select optimal solutions, reducing manual effort and accelerating time‑to‑value. The platform integrates with users' own compute environments (VPC or dedicated resources) and provides a credit‑based usage model that scales with workload demand.
Target Audience
Primary customers are data scientists, ML engineers, and development teams that need to accelerate model experimentation and production deployment while minimizing manual engineering effort.
Features
- Agent‑driven automation of data cleaning, feature engineering, model training, and hyperparameter optimization
- Multi‑step reasoning engine that evaluates multiple solution paths and selects the most effective approach
- Chat‑based interface for guiding NEO through tasks and receiving real‑time recommendations
- Support for building Retrieval‑Augmented Generation (RAG) pipelines and LLM fine‑tuning
- Flexible compute options: bring‑your‑own VPC or use dedicated platform compute
- Credit‑based pricing that scales with monthly usage and includes access to Pro models in higher tiers
- Integration hooks for VS Code and Cursor IDEs to invoke NEO directly from development environments