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Google Cloud Platform > Case Studies > Autonom8: Streamlining Customer Journeys and Reducing Operating Costs with Google Cloud
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Autonom8: Streamlining Customer Journeys and Reducing Operating Costs with Google Cloud

Technology Category
  • Analytics & Modeling - Machine Learning
  • Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
  • Buildings
  • Construction & Infrastructure
Applicable Functions
  • Sales & Marketing
Use Cases
  • Chatbots
  • Intelligent Packaging
Services
  • Cloud Planning, Design & Implementation Services
  • Training
The Challenge

Autonom8, a US and India-based enterprise workflow management software business, aimed to help clients reduce costs and improve customer interactions through automation. The company provides a low-code SaaS platform that includes components like A8Studio, a chat platform, and an analytics module. However, the changing customer demands of enterprises, the rising cost of building software with experienced developers, and the need for customer journeys to evolve due to changing consumer habits and regulations posed challenges. Autonom8 initially deployed on a multinational cloud service, but the decision to opt for a microservices architecture that enables individual services to scale independently while running in a containerized environment demanded high-quality container orchestration. Furthermore, many of Autonom8's clients were financial institutions in India, legally required to retain data within the country's borders.

About The Customer

Autonom8 is a US and India-based enterprise workflow management software business. The company provides a low-code SaaS platform that allows businesses to digitize customer-facing workflows. The platform includes components such as A8Studio, a chat platform, and an analytics module. Autonom8 aims to help clients reduce costs and improve interactions with their customers through automation and enablement of customer journeys. The company's clients include financial institutions in India, which are legally required to retain data within the country's borders. Autonom8's value proposition is to provide a low-code, high-intelligence customer journey automation SaaS platform.

The Solution

To optimize cost, scalability, and performance, Autonom8 began evaluating Google Kubernetes Engine (GKE). After a side-by-side comparison between Google Cloud and its incumbent provider of compute, storage, and other services, Autonom8 found Google Cloud favorable. Google Cloud's global network and local presence allowed Autonom8 to fulfill the data retention requirement of its clients. Autonom8 began deploying on Google Cloud in 2018, with its architecture comprising storage, compute, serverless, container management and orchestration, and Vision AI. The company transitioned fully to Google Cloud, with the number of cloud products and services in its architecture rising from five to about 15. GKE, Vision AI, and Cloud SQL provided the greatest value to the business. Google Cloud also enabled Autonom8 to meet the demands of businesses that wanted to run its platform within their own private data centers.

Operational Impact

  • With Google Cloud, Autonom8 was able to run a platform that accelerates and streamlines customer journeys in a scalable, reliable, cost-effective infrastructure. The use of Google Cloud's Vision AI provided powerful machine learning and optical character recognition (OCR) functionality, supporting a key use case for Autonom8. The company was also able to meet the demands of businesses that wanted to run its platform within their own private data centers. Google Cloud documentation and online resources helped Autonom8 reduce the training needed for new developers to become productive. The company was also able to accommodate the varying service levels required by individual customers based on factors such as the impact of downtime. Autonom8 integrated Google Workspace into its platform, adding new capabilities for clients.

Quantitative Benefit

  • Increased profit margins by up to 30% by switching from a home-grown OCR system to Cloud Vision AI

  • Enables one DevOps team member to manage up to 30 customers

  • Reduces operating costs by up to 20% with localized scalability and architecture through GKE

Related Case Studies.

Case Study
IoT System for Tunnel Construction
The Zenitaka Corporation ('Zenitaka') has two major business areas: its architectural business focuses on structures such as government buildings, office buildings, and commercial facilities, while its civil engineering business is targeted at structures such as tunnels, bridges and dams. Within these areas, there presented two issues that have always persisted in regard to the construction of mountain tunnels. These issues are 'improving safety" and "reducing energy consumption". Mountain tunnels construction requires a massive amount of electricity. This is because there are many kinds of electrical equipment being used day and night, including construction machinery, construction lighting, and ventilating fan. Despite this, the amount of power consumption is generally not tightly managed. In many cases, the exact amount of power consumption is only ascertained when the bill from the power company becomes available. Sometimes, corporations install demand-monitoring equipment to help curb the maximum power demanded. However, even in these cases, the devices only allow the total volume of power consumption to be ascertained, or they may issue warnings to prevent the contracted volume of power from being exceeded. In order to tackle the issue of reducing power consumption, it was first necessary to obtain an accurate breakdown of how much power was being used in each particular area. In other words, we needed to be able to visualize the amount of power being consumed. Safety, was also not being managed very rigorously. Even now, tunnel construction sites often use a 'name label' system for managing entry into the work site. Specifically, red labels with white reverse sides that bear the workers' names on both sides are displayed at the tunnel work site entrance. The workers themselves then flip the name label to the appropriate side when entering or exiting from the work site to indicate whether or not they are working inside the tunnel at any given time. If a worker forgets to flip his or her name label when entering or exiting from the tunnel, management cannot be performed effectively. In order to tackle the challenges mentioned above, Zenitaka decided to build a system that could improve the safety of tunnel construction as well as reduce the amount of power consumed. In other words, this new system would facilitate a clear picture of which workers were working in each location at the mountain tunnel construction site, as well as which processes were being carried out at those respective locations at any given time. The system would maintain the safety of all workers while also carefully controlling the electrical equipment to reduce unnecessary power consumption. Having decided on the concept, our next concern was whether there existed any kind of robust hardware that would not break down at the construction work site, that could move freely in response to changes in the working environment, and that could accurately detect workers and vehicles using radio frequency identification (RFID). Given that this system would involve many components that were new to Zenitaka, we decided to enlist the cooperation of E.I.Sol Co., Ltd. ('E.I.Sol') as our joint development partner, as they had provided us with a highly practical proposal.
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