Category creation is among the most strategically demanding things a B2B technology company can attempt. It requires a definition of the problem clear enough that buyers recognize it as their own, a name for the solution that sticks, and sustained effort to establish both before the market finds its own language. Hud has made category creation the explicit center of its growth strategy, and the appointment of Shai Alani as Vice President of Marketing is the organizational commitment that backs it.
Building a Category Around a Real Problem
The underlying challenge Hud is addressing has been developing quietly for several years and accelerating sharply as AI tools become central to engineering workflows. AI has dramatically increased the pace at which teams generate and ship code. The tools those teams rely on to understand production behavior were built for a different development cadence and were not designed to answer the questions that arise when things go wrong at AI-assisted velocity.
Specifically, observability tools confirm failures. They do not reliably explain them at the level of a specific function, under specific traffic, in the conditions that existed at the moment the code failed. Engineering teams are left to reconstruct what happened from disparate data streams, a process that is time-consuming and often inconclusive.
Coding agents compound the problem. These tools can read a codebase and generate informed suggestions, but they operate without access to runtime evidence of how code actually performed in production. The gap between what an agent knows about the code and what actually happened when that code ran is exactly where debugging breaks down.
Hud’s response is what it calls Runtime Intelligence: production behavior resolved to the function level, paired with deep forensic capability when failures need to be investigated. The company’s pitch is that this layer does not replace observability. It answers the question observability leaves open.
Why Naming the Category Is the Work
In the B2B technology market, the company that names a problem category often defines the terms by which buyers evaluate every solution in that space. Observability itself, application performance monitoring, MLOps, and DevSecOps all began as phrases used by specific companies or communities before becoming standard vocabulary. The companies that owned those phrases early built durable advantages that extended well beyond product features.
Hud is pursuing the same dynamic with Runtime Intelligence. The term does not yet carry broad recognition in the engineering community. Making it the standard phrase that engineering leaders reach for when describing the gap between production failures and root cause understanding is the long-term goal.
“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’s Fit for This Mandate
Shai Alani’s appointment makes sense within this framework. His background at Lightrun, Coralogix, and Aporia spans developer observability, log management, and AI model monitoring, categories where buyers are technically sophisticated and category definitions were earned rather than inherited.
At Hud, Alani takes on global marketing strategy, category creation, brand, and demand generation, with category creation listed explicitly as a core part of his scope.
“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.”
The Stakes of Getting This Right
The engineering organizations Hud is targeting are already operating in the conditions that make Runtime Intelligence relevant. They are shipping AI-generated code at accelerated velocity, experiencing incidents that traditional tools struggle to explain at the function level, and deploying coding agents that lack the production grounding to make reliable diagnostic recommendations.
If Hud can establish Runtime Intelligence as the name for what they are already experiencing, it will have done the hardest part of category creation: connecting a new term to a felt reality. Alani’s role is to drive that connection at scale, consistently enough and credibly enough that the category becomes the market’s language, not just Hud’s.
