Intro
Helping an AI-powered ESG sustainable analytics platform to reduce ongoing maintenance costs.
You can check the previous GaiaLens case study here. The current case study is strictly about DevOps optimization.
About GaiaLens
GaiaLens is a UK-based consultancy firm that provides a web-based application for institutional investors and financial services companies who want to put their money into ESG (ang. Environmental, Social and Corporate Governance).
The platform doesn’t allow investing. Instead, it provides information about publicly listed companies that help calculate factors for each pillar of the ESG. Plus, a main factor that GaiaLens came up with to illustrate the ESG-based credibility of a given company. Based on these factors, the clients of GaiaLens have an opinion about how a given organization acts according to the environmental guidelines, how it treats its employees, whether there are frauds, etc.
GaiaLens is using artificial intelligence to screen companies on greenwashing. AI is leveraged to provide detailed information on organizations that don’t meet ESG demands. GaiaLens already analysed 700 leading Australian companies in regard to their ESG credentials.
Our role in the project
We were asked by GaiaLens to optimize the ongoing process of running and maintaining the application. As of now, the optimization is still in the works and running constantly.
The team was tasked with infrastructure automation (Infrastructure as a Code). We have transformed GitHub runners from manually operated to fully automated. We have also implemented an autoscaling feature. The goal here was to scale the operating time, so they could be shut down when there’s no work to be done.
We have also done all additional work regarding virtual machines, configuration, and maintenance.
Main challenges
We had two main challenges. Previously, the whole backend for optimization was run manually, so our role was based on turning things around and automating the whole process. Since there were two application layers (front-end run by Code & Pepper and back-end data-related), the operation was a little complicated at times. Especially when you consider the data part, which is understandably bigger, given the nature of the application.
The problem is that even with maintenance cost optimization, the large amount of data (storage, processing) requires ongoing attention. So, even when maintenance costs drop, the overall cost of keeping the product live still increases. That’s why it was crucial to automate back-end processes, so the team could focus on the tasks at hand.
Second of all, we had to do it all without documentation, since every piece of knowledge was stored in the heads of in-house specialists. This situation forced us to create a process for reverse engineering, which guided our work and provided the ability to create a working environment for this product. There are many dependencies, data points, and mutual connections between subsystems. We had to work double time to make sure everything works. We “simply” recreated everything that was previously done manually and automated it.
Key tools
Terraform, Ansible, Pulumi, Airflow, RunDeck.
Scope-specific Development Services for GaiaLens
The implementation was primarily based on the following services:
Key Technologies Used in Enhancing the GaiaLens
Essential tools and methodologies were employed to achieve a robust and efficient enhancement:
Exceptional Engineering
Talent Driving
GaiaLens’s Success
Our rigorous hiring process goes beyond mere qualifications, delving into over 70 specific tech skills to identify the very best. By selecting the top 1.6% of tech experts, we ensured that our development services were exceptional, which significantly contributed to GaiaLens’s achievements.
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