Digital Energy & The “Environomics” Approach to Sustainability
Merlin Chacko
SME Stories

Digital Energy & The “Environomics” Approach to Sustainability

With a spotlight on sustainability and technology, the imperative for innovation intensifies, demanding impactful changes. Explore how the startup Digital Energy navigates this intersection, offering solutions through their unique "Environomics" approach.

The evolving nature of the sustainability industry warrants innovative approaches. In today’s SME story, we uncover the journey of Digital Energy, a startup whose very name encapsulates its mission - to seamlessly unite the realms of technology and energy sectors through the application of AI. 

In a candid conversation with the Co-Founder and CTO of Digital Energy, Jimmy Thatcher, we delve into the inspiration behind the company, the uniqueness of their solutions, and the positive impact they have had on sustainability and overall efficiency of organisations.

Bridging the gap: The inspiration

“We saw an opportunity to bridge the gap between the digital world and the energy industry”, says Thatcher. The company’s vision revolves around uniting economic strategies with sustainability practices through the power of AI. This amalgamation gave birth to a unique concept: Environomics.

Digital Energy’s solutions stand out for their modular and adaptable nature. Rather than imposing a one-size-fits-all solution, the company takes a building block approach. Clients can pick and choose the components that suit their needs, aligning with their specific position on the AI journey. This flexibility ensures that clients receive precisely what they need to succeed, without overwhelming them with unnecessary options.

Environomics in practice

To further elaborate on how the Environomics approach works in practical scenarios, Thatcher cites an example from the marine industry.

“Let’s say you have a fleet of offshore supply vessels charged with delivering cargo to offshore platforms. This type of operation is almost always inefficient because there is a mis-match in cargo demand, vessel availability, vessel capacity, type of cargo, platform restrictions, and more. Essentially, there is a lack of coordination between the players.

The solution to that challenge would be to a) capture data about the vessels, cargo, and platforms – this is the track phase, b) visualise that data and gather insights that can be used to build KPIs, set up workflows, or alerts – this is the trace phase, and c) finally use AI to take the data and insights and figure out the best way to use the fleet of vessels to meet demand – this is the optimise phase. 

The beauty of this approach is that each phase, and this is going by the building block approach, provides value to the client. They don’t need to make a big investment in a complex project but can realise value right away. For example, simply using IoT to track where your vessels are in real-time and being able to see what their fuel burn is, can be game changing. Of course the big benefits in terms of cost reductions and efficiency gains come when we connect the AI. For instance, for the scenario described above, we typically see between a 15% to 30% reduction in fuel burn – a significant cost savings for our client.”

Realising sustainable impact

Concrete statistics convey far greater influence than mere theories. Thatcher was happy to share some of their impactful numbers with us, drawing a clear picture of the practical applications and success of their innovative approach. 

“I actually want to highlight something very exciting that we are working on in the realm of steel recycling and circular economy. So, what happens in the steel recycling process is that a vessel (or anything else with a lot of steel) goes through a multistep process of disassembly, smelting, and refabrication before it can be reused. As you might imagine, there are a lot of factors in this process. In fact, there are about 3 million possible recycling paths depending on what combination of facilities, sub-process and transportation type you use. Each path has a different cost and emissions outcome.

What we were able to do is map all the various companies, sub-processes and transportation options, use our emissions database to figure out the associated cost and emissions, and then use AI to find the best possible path given all the possible options. Effectively, we are able to identify the best possible recycling plan in terms of emissions, cost and time.

Using this approach we have seen around a 40% decrease in emissions and around a 30% decrease in cost. The great thing about this is that we are empowering the circular economy – more efficient and more cost effective recycling leads to more of it.”

Client-funded and future-ready

Funding is a crucial aspect for any growing startup. When asked about their funding journey, Thatcher is quick to state, “We are client-funded so far and intend to stay that way unless someone really impresses us”. It is a testament to the trust that their clients have bestowed on Digital Energy, but on the other hand, it also speaks volumes about the efficiency and positive impact that they were able to bring for their clients.

The company is actively engaged in collaborations with government institutions and private entities, teasing exciting developments on the horizon without divulging specifics. Thatcher hints at strategic partnerships, joint ventures, and undisclosed projects with major energy companies, painting an optimistic outlook for their startup’s future.

A glimpse into the future

As advanced technologies continue to shape the future of operations across organisations, Thatcher believes that they are very well positioned for the AI revolution. And this conviction is well-founded, given the company’s pivotal role in driving innovation within the technology and sustainability spaces - two critical focus areas in the global landscape moving forward.

The confidence that Thatcher has in their solutions is infectious. He concludes on a positive note, “Not only are we already working in this space, but we have secured partnerships with companies like NVIDIA and Microsoft that allow us to access some really good AI infrastructure. We also are now pretty adept at all types of AI including the newer stuff like large language models. We have actually built a couple of those that are industry specific and we are excited to see how our clients put them to use. So I am probably more excited now than I have ever been, and I know all of Digital Energy feels that way.”