Arrk helped improve the growth efficiency by 25% and improve productivity by 50% with the implementation of the double tag team of Amazon Sagemaker and AWS Cloud Services to facilitate challenges faced in the data management process


Our client is one of the leading players in the construction data businesses, specializing in offering comprehensive solutions to professionals in the construction industry. They are headquartered in Cheshire, UK, and offer focused data-driven insights to help shape the construction industry landscape.

We have been partners with our client since 2005 to help mark the beginning of collaborative efforts to improve their data management facilities.

Construction Data Business

Partners Since 2005

Partnership Since 2017

Problem Statement

Our client was faced with multiple challenges in the data management process that required a complete overhaul of their tools. The existing web scraping system needed the manual setup of machines, which was not only time-consuming but also needed high levels of manpower. This meant that the development cycle was hampered, and there was loss of data.

Because of the machinery used, it meant that only one developer could work on one machine at a time. This not only hampered the collaborative efforts between teams but also impeded parallel development processes.

Also, the costs associated with the total setup and maintenance of the machines for developers were fairly high, which led to financial problems. In fact, our client found it difficult to allocate resources away from these machines, which in turn caused a suboptimal balance between the operational aspect and cost

Data Loss

Robust System

Hindered Team collaboration

LLM and ChatGPT

Solution Development

We at Arrk took a look at the problems our client was facing and tailor-made a solution leveraging two tools – Amazon Sagemaker and AWS Cloud Services. As per our initial assessment, we introduced Amazon Sagemaker into the machine setup process. This reduced the configuration time, especially with GPUs, and better extraction of data.

We then collaborated with Sagemaker and AWS Cloud Services to ensure that multiple developers could work on diverse projects at the same time. This started fostering an environment of innovative and collaborative efforts to improve the efficiency and speed of the development team as a whole.

Finally, by integrating the AWS Cloud Service, we brought in scalable infrastructure that made our client adapt dynamic workloads for the developers. By doing this, the company achieved substantial savings and
could optimize its resource allocation without having to compromise on its productivity.

Sagemaker + AWS

Collaborative Efforts

Scalable Infrastructure

Resource Optimization


  • A substantial increase in efficiency by 25% led to the sorting and management of larger volumes of data.
  • Reduction in costs by 30% p.a., particularly in the machine setup and allocation of resources stages.
  • Various developers could not collaborate and innovate within the team effortlessly.
  • Teams could now focus more on the business problem aspect rather than infrastructure-related tasks.
  • Workloads could now be managed effortlessly without compromising the performance of teams, and productivity improved by 50% without any added costs.

Cost Reduction

Managed Workload

Improved Productivity

Cost Reduction

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