Client Profile

Customer Name: Energy Provider

Customer Since: 2022

Sector: Energy


Working with AWS and one of the world’s largest energy solutions providers, Version 1 were engaged to transform the customer’s services from a legacy platform to a new platform hosted on AWS. The objective was to provide the best service to their customers and support customer retention including the use of AI Chatbots and Salesforce integration.

The Challenge

The customer was experiencing numerous issues on their legacy platform including pressure to move from manual processes responsible for inhibiting new sales to customers, a lack of definition of workloads leading to bottlenecks, high-cost processes and a loss of data and KPIs not being accurately recorded.

A new platform on AWS would be the trigger for the customer to resolve their priority issues. Version 1 partnered with AWS to remove obstacles and provide proof of concept activities that would enable innovation.

Why Version 1?

Version 1 have a strong history of collaboration with the Energy and Utilities Sector. We are experts in Cloud, AWS and Oracle and have a large team of consultants with experience in a variety of technology domains. Having won AWS Migration Partner of the Year, Version 1, have a strong endorsement and credibility to transform complicated workloads to cloud platforms.


Version 1 partnered with AWS to manage, create and support the customer transformation. Along with the technical delivery we provided cultural and process enablement by guiding on the implementation of Agile and DevOps processes across the programme.

We captured the internal processes, current and future architectural states, high-lighting pain points and bottlenecks. There were numerous engineering inefficient and over-provisioned resources. Where appropriate, cloud-native solutions were provided including the use of Serverless, API and NoSQL technologies all deployed through the use of Infrastructure-as-Code (IaC; Terraform).

We improved the Direct Sales function with enhanced product and customer information using cached produced catalogues, integrated via a Salesforce API and DynamoDB. KPI and data capture was increased and stored for analytics and business intelligence. Customer journey lifecycles were monitored and stateful to improve customer satisfaction.

We provided PoCs of seamless switching between Salesforce and Amazon Lex (conversational AI) chatbots to provide customer service agents with the latest and personalised information. Amazon Connect (AWS’ cloud contact centre) was configured and deployed to improve agent productivity and customer experience.

A centralised digital communication platform was implemented to manage all communication including letters, SMS, emails and other digital chat application messages. The communications were integrated via APIs with the use of Mulesoft to Salesforce. The solution was GDPR compliant to ensure the protection of customer data.

To improve solution observability the workloads were integrated with Datadog to provide real-time data collection, a unified view via a single pane of glass and data insights.

We supported financial forecasting on the cost of technical deployments and developed a cost calculation model which supported the customer in forecasting cloud costs based on storage costs and data compression levels.

Real Differences, Delivered

  • The use of seamless switching of AI-powered chatbots improving agent and customer experiences
  • A unified customer messaging platform providing a single point of customer reference
  • An API event-driven solution with product caching to reduce latency to information and costs
  • Right-sizing over-provisioned resources to reduce costs and improve performance
  • Improved insights and end-to-end observability of workload processes
  • A cloud-native approach, making use of AI, Serverless, Contact Centre and PaaS technology
  • Building a custom cost model allowing the customer to forecast cloud costs
  • Cultural and technical changes to the programme and customer
  • A detailed current and future state of the workload functions and underpinned technology


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