Hud

Provides Runtime Code Insights to Help AI Coding Tools Analyze Production Behavior

Business Software
Private

Company Overview

Snapshot

Founded in July 2023 by Roee Adler and May Walter, Hud operates with 11–50 employees. The company has raised $21,000,000 across one funding round. In December 2025, Hud unveiled its real-time production visibility solution for engineers and AI agents.

Business overview

Hud develops a runtime code sensor designed to enhance AI coding tools by analyzing production behavior. Its core technology allows for real-time, function-level data insights without requiring manual instrumentation, configuration, log analysis, or dashboards. Hud addresses the discrepancy between AI-generated code and its actual performance in production environments, serving the business software sector and specifically targeting software development within enterprise and professional services.

Strategic signal

In December 2025, Hud launched a runtime code sensor that streams real-time information to AI agents, aiming to prevent malfunctions in complex environments. This development signals Hud's strategic focus on bridging the gap between AI-assisted code generation and reliable production behavior, offering a critical tool for developers and AI systems to ensure code quality and operational stability.

Company Intelligence Q&A

What is Hud's primary offering?
Hud provides a runtime code sensor that helps AI coding tools analyze production behavior, delivering real-time, function-level data without manual instrumentation.
When was Hud founded and by whom?
Hud was founded in July 2023 by Roee Adler and May Walter.
What was a key announcement from Hud in December 2025?
In December 2025, Hud unveiled its real-time production visibility solution, designed for both engineers and AI agents, and launched a sensor to stream real-time data to AI agents to prevent malfunctions.
How does Hud address challenges in AI-generated code?
Hud aims to address the difference between code generated by AI assistants and the actual behavior of that code when running in production, providing insights to prevent issues.