Hud, the Runtime Intelligence company, has appointed Shai Alani as Vice President of Marketing. The hire arrives at a moment when engineering organizations are accelerating their adoption of AI-native development workflows and facing a growing need to understand what their code is actually doing once it reaches production.
Alani brings a background in high-growth B2B technology marketing, with prior roles as VP Marketing at Lightrun and marketing leadership positions at Coralogix and Aporia. At Hud, he takes on responsibility for global marketing strategy, category creation, brand, and demand generation.
The Problem Hud Is Solving
The context behind the appointment matters as much as the appointment itself. AI tooling has fundamentally changed the pace at which engineering teams ship software. More code moves into production, more frequently, and the pressure to ship fast has not been matched by equivalent improvements in the tools teams use to understand production behavior when something goes wrong.
Traditional observability platforms are effective at detecting failures. They surface alerts, aggregate logs, and provide visibility into system health. What they do not do is explain failures at the level of a specific function, under specific traffic, in the conditions that existed at the moment the problem occurred. When an incident happens, engineering teams are typically left reconstructing events from scattered data streams. The process is slow, expensive, and heavily dependent on whether the right instrumentation was in place before the failure happened.
That reconstruction problem is amplified significantly in AI-native development environments. Coding agents can read a codebase and generate fixes, but they operate without access to runtime evidence of how code behaved under real production traffic. They have knowledge of what the code says, but not what the code did. In a debugging or validation context, that distinction is critical.
Hud describes its solution as Runtime Intelligence: production behavior resolved to the function level, combined with deep forensic capability when failures require investigation. The goal is to give both engineering teams and their coding agents the evidence they need to detect issues early, identify root causes accurately, validate changes, and ship with confidence.
Leadership Perspective
“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.”
Alani described Runtime Intelligence as the layer the current AI software stack is missing.
“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.”
What Alani Brings to the Role
Alani’s background across Lightrun, Coralogix, and Aporia positions him well for the specific challenge Hud faces. Each of those companies operates in spaces directly adjacent to developer observability and AI monitoring, and each required communicating technically grounded value propositions to engineering audiences. The ability to translate complex infrastructure capability into clear market positioning is precisely what category creation demands, and Alani has done versions of that work across multiple product categories.
At Hud, category creation is listed explicitly as part of his scope. That signals Hud’s intent to move from product-building into active market definition. Runtime Intelligence does not yet carry the same instant recognition as observability or APM. Making it a term that engineering leaders reach for naturally, when describing a problem they already experience but may not yet have language for, is the long-game objective.
The Broader Shift Driving Hud’s Timing
The timing of this appointment reflects a structural change in software engineering. As coding agents become embedded in development workflows, the volume of AI-generated code moving into production environments is increasing. Engineering teams are shipping more, faster, and with less manual review at each stage. The downstream consequence is that when failures occur, the path to root cause is harder to navigate, not easier.
Hud’s pitch to engineering organizations is that the answer is not more logs or more alerts. It is function-level runtime evidence that gives both humans and agents a clear picture of what the code was actually doing when something went wrong. With Alani now leading marketing, Hud is positioned to take that argument to the broader engineering community and into the conversation about what AI-native teams actually need to operate reliably at speed.