Vieolo's commitment to reduce its carbon footprint

As the world becomes more aware of the impact of climate change, many companies are taking steps to reduce their carbon footprint, and here at Vieolo, we are no exception.


As the world becomes more aware of the impact of climate change, many companies are taking steps to reduce their carbon footprint, and here at Vieolo, we are no exception.

Our main operation in Vieolo is to automate and optimize the workflow and internal processes of the companies. In our journey so far, we have eliminated the need for millions of pages of physical paper and reduced the energy consumption of our users and clients significantly in various ways.

Regardless of the overall positive effect of our work, we recognize that our operations have a negative impact on the environment, and we are committed to doing our part to reduce our carbon emissions.

While offsetting our energy consumption through carbon credits or other mechanisms can be an important part of our overall sustainability strategy, we believe that reducing our energy consumption should be a top priority.

There are fundamental problems with the current approach to carbon offsetting. The effectiveness of carbon offsetting depends on the assumption that the offset project would not have happened without the offsetting investment. However, this is difficult to prove, and there have been cases where offset projects have been found to have been implemented even without offset funding.

Additionally, there are currently no widely accepted criteria for a successful carbon-offsetting project. The effectiveness of a project mostly depends on the claims of the vendor and we cannot independently verify their effectiveness.

As a result, choosing an offsetting project, at the time of writing, involves picking a project at random and relying on chance. In our case, using a carbon offset project would be more of a publicity stunt rather than a genuine effort. So, the best strategy for a firm like Vieolo is to prevent unnecessary energy consumption in the first place.

There are several strategies that we have been following since the start of 2022 that not only reduce our energy consumption but will also reduce the energy requirements of the products and services we offer to our users and clients.

The right tools

One of the most fundamental changes that we made was to start using programming languages that are memory and CPU efficient.

Many of the popular programming languages rely on runtimes or virtual machines to run a piece of software. These runtimes provide a layer of abstraction and convenience to the developers which makes them quite popular. However, the convenience of these languages comes at the cost of a high memory footprint and higher CPU usage. We, on the other hand, have gradually migrated toward languages that do not rely on runtimes, hence are much more efficient in their memory and CPU usage.

On a small scale, the wasted energy on unnecessary memory is negligent. However, every piece of software that we write might end up running on millions of devices around the world. Considering the fact that our advancement in technology relies on the existing technical debt, every small decision will have large consequences down the road.

However, just like any other group of developers, we have to use certain languages or technologies that are not the most efficient option. In those cases, our main focus is on optimizing the existing tools to perform as efficiently as possible.

Data Transfer

In today's world, we transfer data all the time. This transfer of data is the foundation of the internet and modern software architecture. But this data transfer is not free.

Similarly, the transfer of a piece of data on a small scale is not an issue but we write applications and solutions that, each, are used by thousands of users for many years. As the usage of our solutions scales up, so does the energy required to transfer the necessary data.

The first and easiest solution is to store the data in the nearest data center to reduce the distance between the data storage and the user. This approach has the advantage of providing a better user experience and in some jurisdictions, such as the EU, is a legal requirement.

The second approach is to reduce the amount of data to be transferred. This idea might sound simple but is much more complicated in practice. However, we have found workarounds in non-critical situations to fetch the necessary data when it’s needed rather than in anticipation of future behavior without compromising the user experience.

Use AI when it's actually needed

The advancements in Artificial intelligence are among the biggest achievements of modern engineering, solving unique problems that conventional architectures cannot solve.

To produce an AI model, we have to analyze a very large set of data. The AI model will use the analysis of the dataset to complete a certain task when presented with a new piece of data that was never seen before. This process is known as training.

Training an AI model is a very energy-intensive process. According to research by the University of Massachusetts Amherst, training a single AI model can emit as much carbon as five cars in their lifetimes. This figure is for a single training run while some AI models require thousands of repeated runs to achieve a high accuracy.

So, before thinking about training a new AI model, we ask ourselves two questions. If the answer to both of these questions is no, then only we start the process of training a custom AI model.

Can the problem at hand be solved effectively by conventional software? If yes, we stick to the conventional software. A conventional solution is almost always more energy efficient than an AI model and even if the solution is slightly less efficient than a trained AI model, the solution might never be used enough to surpass the energy used in training.

Can the problem be solved effectively by a pre-trained AI model? If yes, we use a pre-trained model. Over the years, many developers have trained various AI models to solve common problems to an acceptable level. Using a pre-trained model will save the enormous amount of energy required to train a model from scratch.

The energy consumption of the training process is one of the important challenges that the tech industry, including us, has to solve in the upcoming years.


At Vieolo, we believe that sustainability is not just an obligation but also an opportunity. By reducing our carbon footprint, we are not only doing our part to protect the environment but also creating a more resilient and sustainable business.

We acknowledge that the strategies we currently follow might be flawed and require changes in the future. However, we are proud of the progress we have made so far, and are committed to continuing our efforts to reduce our carbon footprint.