A prototype in 2 weeks,
and a route for every case after it.
How Carlsberg turned automated error reporting into a repeatable route from operational data to production AI, reusable across every brewery use case.
The problem.
Operators were flagging equipment malfunctions, process anomalies and quality deviations through spreadsheets, emails and local practice. The practice varied by site.
Without a common pattern for reporting, there is nothing to apply AI to.
Breweries generate large amounts of operational data across machines, sensors and production lines, but reporting on problems was manual, reactive and inconsistent. The consequences were the ordinary ones: delayed visibility, fragmented communication, uneven follow-up.
The structural consequence mattered more. With no shared way to capture and triage problems, there was no way to spot a systemic issue, compare sites, or apply AI meaningfully.
The prototype.
Heyra introduced its Problem, Prototype, Production framework with Carlsberg's AI team, mapping the bottlenecks in error reporting, prioritising candidate use cases, and choosing automated error reporting as the flagship.
Using Lovable, the prototype connected real operational data to a structured workflow, defining how an issue is captured, enriched with context and routed to the right team. It produced the data flows, interaction design and governance patterns together, rather than leaving governance to be added later.
In production.
Beyond the prototype, production architecture, governance and checkpoints for scaling were defined.
The framework now describes how any similar operational AI use case moves from idea to production: what data is needed, how success is measured, how risk is managed.
Automated error reporting is the first application of that pattern, not the end of it. AI work shifted from one-off experiments to a structured delivery model.
The value of the flagship is the route it leaves behind: the next use case starts from a proven pattern instead of from zero.
Change how
you operate.
Tell us where you’re stuck. If operational AI can change the outcome we’ll show you how, and if it can’t, we’ll say so.
Talk to us