Building the Future of Clean Energy: Our Artificial Intelligence Applications Technology Stack

At Eshmun AI, our commitment to innovation is not just about the solutions we develop but also the artificial intelligence application technology stack we use to build them. We’ve carefully selected a cutting-edge technology stack that supports scalability, flexibility, and performance to drive efficiency in the renewable energy sector and deliver on our mission. From microservices architecture to domain-driven design, from Rust to Julia, every decision we’ve made reflects our vision for the future of energy. This blog post explores our technology stack and the reasons behind our choices.

We are building a cutting-edge artificial intelligence applications technology stack

1. Rust and Microservices: Performance and Scalability at the Core

Our platform is written primarily in Rust, a systems programming language known for its performance and safety. Rust’s memory safety guarantees and zero-cost abstractions make it ideal for building high-performance applications that need to handle large-scale, real-time operations—perfect for the renewable energy sector, where processing vast amounts of telemetry data from wind turbines and solar farms is essential.

We’ve adopted a microservices architecture to enhance scalability and flexibility further. Microservices allow us to break down the platform into independent, loosely coupled services, each responsible for a specific function. This approach enables faster development, better fault isolation, and more flexibility in scaling individual components as needed. Microservices also make updating and maintaining the platform easier over time, without disrupting the entire system.

To ensure smooth deployment and management of these services, we use Docker containers, which package each microservice with its dependencies, and Kubernetes to orchestrate and manage these containers at scale. Kubernetes provides automation in deploying, scaling, and operating the microservices, making the platform resilient and highly scalable.

2. Domain-Driven Design: Aligning Technology with Business Needs

Our design approach follows domain-driven design (DDD) principles, which help us structure the platform around the core business concepts of the renewable energy industry. DDD ensures that the platform architecture is aligned with the needs of our users—whether they are energy producers, grid operators, or maintenance teams. By modelling our software based on business domains, we ensure that each microservice corresponds to a specific business capability, making it easier to evolve and adapt the system as the market grows and changes.

This approach also allows us to keep our focus on solving the real-world challenges of the renewable energy sector, such as optimising energy production and managing energy storage. It also smooths communication between developers, business analysts, and energy experts, as everyone works within a shared understanding of the domain.

3. BigQuery and Druid: Handling Big Data Efficiently

In a sector with an enormous volume of data, choosing the right data infrastructure is crucial. We use BigQuery as a data warehouse and Apache Druid for real-time analytics. BigQuery is ideal for handling large datasets, enabling fast SQL queries and providing a scalable solution for storing and analysing massive amounts of telemetry, weather, and grid data.

On the other hand, Druid is designed for high-performance, real-time analytics on streaming data. This makes it perfect for our platform, where real-time decisions—such as adjusting wind turbine operations or managing energy storage—are critical to maximising efficiency. The combination of BigQuery and Druid allows us to perform both batch and real-time analytics, offering the best of both worlds for predictive and reactive data management.

4. WebAssembly and WebComponents: Future-Proofing the Front-End

On the front end, we use WebAssembly (Wasm) and WebComponents to build a highly performant and flexible user interface. WebAssembly allows us to run near-native code directly in the browser, resulting in significantly faster performance compared to traditional JavaScript applications. This is crucial for delivering a seamless user experience, especially for data-heavy applications that need to visualise complex energy production and consumption metrics in real-time.

WebComponents provide a modular and reusable component-based architecture for the front end. They allow us to build custom, encapsulated HTML elements that can be reused across different parts of the application. This leads to better maintainability, improved performance, and the ability to adapt to evolving web standards. Together, WebAssembly and WebComponents give us the flexibility and speed required to deliver a user-friendly interface that scales with our platform’s growing complexity.

5. Julia: Powering Machine Learning for Energy Optimisation

For machine learning, we’ve chosen Julia, a high-performance programming language designed for numerical and scientific computing. Julia’s unique combination of speed and ease of use makes it perfect for developing the advanced deep reinforcement learning algorithms that power our AI solutions.

Julia is particularly well-suited for our work in renewable energy because of its ability to handle complex mathematical computations at high speed. This allows us to quickly iterate on machine learning models and deploy them into production. The result is faster wind turbine performance optimisation, more accurate energy demand forecasting, and smarter energy storage management—all essential for improving the capacity and load factors of renewable energy assets.

6. MLOps and IDP

In addition to our cutting-edge technology stack, we are also developing our own MLOps and Internal Developer Platform (IDP), driven by our dissatisfaction with existing commercial and open-source solutions. For our MLOps platform, our guiding principles are simplicity and speed. We aim to streamline the entire MLOps pipeline, from model development to deployment, ensuring fast iteration and deployment cycles without compromising on governance. Our platform offers end-users simple tools to locate models from our extensive model zoo, fine-tune them for specific applications, and deploy them efficiently. At the same time, we emphasise strong governance, logging, and metrics to ensure transparency, performance tracking, and compliance at every stage of the model lifecycle. By building our own MLOps solution, we can fully control the user experience, optimise for our unique needs, and provide a seamless and robust machine learning infrastructure for our AI solutions.

Conclusion: Building the Future of Clean Energy with the Right Technology

At Eshmun AI, our artificial intelligence applications technology stack is a testament to our commitment to innovation and excellence. By combining Rust for performance, microservices for scalability, BigQuery and Druid for efficient data management, and Julia for machine learning, we are building a platform that is both cutting-edge and future-proof. Our use of WebAssembly and WebComponents ensures a fast and flexible front end, while domain-driven design ensures that our technology remains aligned with the needs of the renewable energy sector.

Our stack is designed to solve the inefficiencies plaguing renewable energy, driving long-term value for our customers, partners, and investors. By building a robust, scalable platform, we’re ready to revolutionise how the world generates and consumes clean energy, one microservice at a time.