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Crayon

Crayon powers 2 source-linked AI deployments documented in AIUseCaseHub, across 2 industries and 2 countries.

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Use Cases

2

Industries

2

Countries

2

Hyperscaler mix

Filter Crayon's implementations by cloud provider evidence.

How Crayon builds AI

Build / Buy / Compose across this partner's documented cases

BuildBuyComposeMixed

2 of 2 cases classified (100%) · Compare all use-case types

Evidence persistence

1 of 1 judgeable case is still publicly referenced · 1 show the organization expanding AI use.

Durability of public evidence, not whether systems remain in production. How this is measured →

All Use Cases (2)

Microsoft

Coca-Cola Optimizes Bottling Line Efficiency with AI-powered Spare Parts Recognition

Coca-Cola HBC, a leading European and African beverage bottler, partnered with Crayon to address production line inefficiency due to slow identification and sourcing of spare parts. They implemented an innovative AI-driven image recognition app developed with Microsoft technology and Crayon's expertise. This app enables faster, accurate identification of crucial spare parts across multiple sites, ensuring smoother bottling operations. The project has been rolled out in phases across European and African locations, enabling streamlined operations and delivering measurable productivity improvements. The solution demonstrates tangible business value and achieved significant ROI in less than two years.The image recognition app was developed and deployed specifically for production line environments.This collaboration has dramatically sped up maintenance processes by quickly matching images of parts to internal sourcing databases or catalogs.Real-time part identification is expected to minimize production downtime and reduce manual errors.

ManufacturingGlobal
Microsoft

DNV GL streamlines maritime certification with AI-powered solutions

DNV GL, a global leader in shipping certification, partnered with Crayon to leverage artificial intelligence and machine learning for operational efficiency. The collaboration sought to address complex, labor-intensive processes such as estimating ship inspection times and managing technical documentation across vast archives. By implementing Microsoft Azure Machine Learning, DNV GL automated time estimation for inspections, and developed algorithms to match technical documents spanning 150 years of records.The joint initiative resulted in over ten machine learning solutions being deployed in production, significantly reducing the manual workload of experts and accelerating business processes. The partnership also established an AI Center of Excellence, facilitating a robust community of practice and furthering AI-driven innovation internally and for commercialization. Crayon’s recruiting strategy, focusing on aptitude and cross-disciplinary skills, supported the rapid expansion of its AI team and the scale of impact on DNV GL’s business transformation.By systematically applying data science and machine learning, DNV GL strengthened its global service offerings and exemplified the practical benefits of AI in the maritime industry. Their joint journey has positioned both companies at the forefront of digital transformation for industrial certification and compliance.

LogisticsNorway