Hud has appointed Shai Alani as Vice President of Marketing, adding a senior go-to-market leader as the company works to establish Runtime Intelligence as a core layer in modern software development. The company builds a platform that captures function-level production behavior, giving engineering teams visibility into how code actually runs once it is deployed.
At a time when AI tools are accelerating how quickly software is written, Hud is focused on a different part of the lifecycle: what happens after code reaches production. Its platform is designed to surface detailed runtime evidence that helps engineers and AI coding agents understand incidents, trace root causes, and validate fixes based on real execution data rather than incomplete logs or scattered telemetry.
With Alani joining the leadership team, Hud is strengthening its efforts to define and scale this category.
A Senior Hire Focused on Category Definition
Alani brings experience building marketing strategies for developer infrastructure companies. He previously served as VP Marketing at Lightrun and held marketing leadership roles at Coralogix and Aporia. At Hud, he will lead global marketing, category creation, brand positioning, and demand generation.
His role reflects Hud’s broader ambition: not only to expand adoption of its platform, but to frame how the industry understands the relationship between AI-generated code and production behavior.
“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.”
Why Runtime Still Remains the Hardest Problem
Despite rapid adoption of AI coding assistants, the hardest problems in software engineering remain tied to production systems. Teams can generate code faster than ever, but when systems fail, understanding why they failed still requires digging through logs, metrics, and distributed traces that often fail to tell the full story.
Hud argues that this gap is even more pronounced in AI-assisted development. While coding agents can reason about source code, they do not inherently understand how that code behaves under real production conditions. That missing context limits how effectively they can assist in debugging and remediation workflows.
The company’s Runtime Intelligence platform is designed to address this by capturing function-level execution data directly from production environments. When issues occur, Hud collects forensic context that reconstructs what actually happened at the point of failure, allowing engineers to move from signal to root cause with less guesswork.
From Observability to Runtime Evidence
Traditional observability tools typically focus on detecting that something has gone wrong. Hud is positioning itself in a different layer of the stack that is centered on explaining why it went wrong using execution-level evidence.
By instrumenting production at the function level, the platform creates a continuous stream of runtime data that can be used during incident response. According to the company, this approach reduces the need to manually piece together logs or rely on incomplete telemetry when diagnosing issues.
The result is a workflow where production behavior becomes an active input into development and debugging, rather than a retrospective artifact analyzed after the fact.
Building a Category in Real Time
For Alani, the opportunity lies in defining how the industry thinks about this shift.
“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.”
Early Adoption and Market Positioning
Hud’s platform is already deployed across millions of production services at companies including Monday.com, Lemonade, Axonius, and Cyera. The company is backed by $21 million in funding led by Aleph and SquarePeg.
As AI continues to reshape how software is built, Hud is betting that production visibility will become just as important as code generation itself. With Alani leading marketing, the company is now focused on turning Runtime Intelligence from an internal product definition into a widely recognized category across engineering teams building AI-native systems.