Hud, which builds a Runtime Intelligence platform designed to capture function-level production behavior for engineering teams, has appointed Shai Alani as Vice President of Marketing. The hire comes as the company continues to scale its platform across organizations building and operating software in production environments.
Rather than focusing solely on observability after incidents occur, Hud’s approach centers on capturing what actually happens inside production systems at the function level. The company says this allows engineers and AI coding agents to understand failures with greater precision, identify root causes faster, and validate fixes before deploying changes.
With the addition of Alani, Hud is strengthening its go-to-market and category creation efforts around Runtime Intelligence, a term it uses to describe its approach to linking production behavior directly to code execution.
Building Around Production Reality
Alani joins Hud with prior experience leading marketing organizations at developer-focused infrastructure companies. He previously served as VP Marketing at Lightrun and held marketing leadership roles at Coralogix and Aporia. At Hud, he will oversee global marketing strategy, brand positioning, category development, and demand generation.
The appointment reflects Hud’s intention to expand awareness of how runtime-level production data can be used not just for debugging, but as part of the broader software development lifecycle.
“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 Execution
As AI coding tools become more widely adopted, engineering teams are shipping more code at faster rates. But according to Hud, the challenge of understanding production behavior has not been eliminated, and in some cases, it has become more complex.
While AI systems can generate code and assist with debugging at the source level, they still lack visibility into how that code behaves under real production traffic. Traditional observability tools can indicate when something has gone wrong, but often require engineers to reconstruct events using logs and distributed telemetry.
Hud’s platform is designed to address this gap by embedding a runtime code sensor alongside every function in production. When an issue occurs, the system captures detailed forensic context that can be used to trace exactly what happened, determine the root cause, and validate potential fixes before deployment.
The company positions this as a shift from reactive debugging to evidence-based investigation, where production behavior becomes a structured input into the development process rather than an after-the-fact signal.
A Marketing Mandate Focused on Category Creation
For Alani, the opportunity at Hud extends beyond traditional marketing leadership. His mandate includes helping define and scale Runtime Intelligence as a category in its own right.
“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.”
Expanding Adoption Across Engineering Teams
Hud reports that its Runtime Intelligence platform is already in use across millions of production services at companies including Monday.com, Lemonade, Axonius, and Cyera. The platform is backed by $21 million in funding led by Aleph and SquarePeg.
As adoption of AI-assisted development continues to grow, Hud is positioning itself around the idea that production understanding will become a critical part of how software is built and maintained. With Alani leading marketing efforts, the company is aiming to bring that positioning into broader industry awareness while scaling its presence among engineering teams building AI-native systems.
