Hud Appoints Shai Alani as VP Marketing to Champion Runtime Intelligence for AI Development

Artificial intelligence has dramatically increased the speed at which software can be written, but it has not fundamentally changed how engineers investigate failures after applications reach production. As organizations embrace AI-native development, many are discovering that faster code generation does not necessarily translate into greater confidence in deployed software.

That disconnect is drawing attention to a growing challenge within the software development lifecycle: giving engineers and AI coding agents the runtime evidence needed to understand what actually happened when production systems fail.

Hud believes this missing visibility represents a new category of technology, one it calls Runtime Intelligence.

A Leadership Hire Aligned With a New Market

To expand that vision, Hud has appointed Shai Alani as Vice President of Marketing.

Alani brings experience leading marketing organizations at several B2B software companies, including Lightrun, where he served as VP Marketing, as well as previous marketing leadership positions at Coralogix and Aporia. At Hud, he will oversee global marketing strategy while driving category creation, brand development, and demand generation.

The appointment comes as software organizations increasingly integrate AI into their development workflows while searching for better ways to understand application behavior once code reaches production.

"AI has changed the speed of software creation, but production is still where code proves itself," said Roee Adler, Co-founder and CEO of Hud. "The next major category in the AI SDLC is Runtime Intelligence: production behavior resolved to the function level, coupled with deep forensics when things go wrong, so humans and agents can understand, fix, and validate software with confidence. Shai brings the experience we need to build that category and scale Hud into a defining company for AI-native engineering teams."

Closing the Gap Between Code and Production

While AI coding agents can analyze repositories and generate new code, Hud argues that they remain limited by the same challenge facing human developers: they cannot determine how software behaved in production without access to runtime evidence.

Traditional observability platforms can identify that an application has encountered an issue, but tracing the root cause often requires searching through logs and multiple telemetry sources. According to Hud, this approach can be slow and may still fail to reconstruct the sequence of events that produced the problem.

Its Runtime Intelligence platform is designed to address that issue by running a runtime code sensor alongside every function in production. When an incident occurs, the platform captures detailed forensic context intended to help engineers and AI coding agents understand what happened, identify the precise root cause, validate a solution, and merge code with greater confidence.

Defining the Next Layer of the AI Software Stack

For Alani, the growing adoption of AI-assisted development makes runtime visibility increasingly important rather than less.

"Runtime Intelligence is the missing layer in the AI software stack," said Shai Alani, VP Marketing at Hud. "AI has made it easy to generate code, but it has not made it any easier to stand behind that code once it is running in production, where reliability is actually decided. That gap is fast becoming one of the defining problems for AI-native engineering teams, and it is exactly the kind of category you build a company around. That is why I joined Hud, and it is the story I am excited to take to market."

Hud says its platform already supports engineering teams operating millions of production services. Customers include Monday.com, Lemonade, Axonius, Cyera, and others. The company has also secured $21 million in funding led by Aleph and SquarePeg.

As AI continues to reshape software engineering, attention is increasingly turning to technologies that help validate software after it has been generated. Hud is betting that Runtime Intelligence will become an important component of that evolution, providing production-level evidence that enables both engineers and AI coding agents to build, investigate, and deploy software with greater confidence.