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Home/MLOps & Model Deployment/Dawnfield Industries
Manufacturing Case Study

Dawnfield Industries

A serving infrastructure rebuild that handled a major spike in prediction volume.

-30%Downtime
Dawnfield Industries digitallyscaled
ClientDawnfield Industries
IndustryManufacturing
Timeline10 weeks
ServicesMLOps & Model Deployment
The Challenge

Where things stood before

Dawnfield Industries's biggest obstacle was limited visibility into production issues until they'd already become expensive — something their team had tried to patch more than once without lasting success.

It was the kind of issue that showed up in day-to-day frustration long before it showed up in a report.

The Solution

What we built

Rather than jumping straight to a model, we spent time understanding the specific shape of the problem first.

What we ultimately delivered was a serving infrastructure rebuild that handled a major spike in prediction volume, built specifically around what Dawnfield Industries needed.

  • Designed around Dawnfield Industries's actual data and workflow, not a generic template
  • Benchmarked against clear, agreed-upon success metrics from day one
  • Validated thoroughly before deployment to avoid production surprises
  • Delivered within a 10-week timeline without cutting scope
Results

The impact, by the numbers

-30%
Downtime
+46%
Defect Detection Rate
-19%
Inspection Time
+22%
Throughput
Tech Stack

Built with

MLflowKubernetesDockerAWS SageMaker

“I was skeptical this would actually work for us specifically. But -30% downtime within a few months, and the process to get there was more thoughtful than I expected.”

HN
Hakon Novikova

Head of Data, Dawnfield Industries

Next Project

Alderfield Software

View Case Study

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