AI has enormous potential to improve lives and business. At the same time, its environmental impact is often invisible but significant. Large language models can consume a lot of energy, and each query increases carbon footprint.
At Trail Openers we help organizations use AI in ways that are efficient, transparent and sustainable.
AI impacts must be considered from two perspectives:
Footprint: the energy used in model development, training, and inference.
Handprint: the positive impact AI can enable — smarter logistics, energy-saving forecasts, and reduced waste.
Our goal is to maximize handprint and minimize footprint in every implementation.
What does sustainable AI mean in practice?
Responsible use of AI always starts with the process. First, we understand the customer’s goal and assess where AI might provide value. Then we evaluate whether AI is truly needed—and if so, what type of technology fits best: a small language model, classical machine learning, or a rules-based system.
We don’t assume every use case requires a large language model. Often, a smaller and more focused solution delivers greater value with fewer resources.
We design the architecture to use as little energy as possible, leverage low-emission cloud infrastructure, and perform efficiently over time. The result is an AI solution that creates real value—without unnecessary environmental impact.
People remain at the center of every AI solution we build. We automate processes intelligently but ensure that human oversight remains in place for decisions that involve ethical, legal, or critical consequences. In this way, AI supports people—it doesn’t replace them.
Our services in sustainable AI
AI sustainability assessment
Evaluate your current or planned AI stack for energy use, carbon emissions, and efficiency opportunities.
Green AI architecture
From GenAI models to ML pipelines, we help you choose and build AI systems that work — and waste less.
Integration & reporting
We integrate emissions tracking and sustainability dashboards directly into your AI workflows.
Trainings and workshops
Equip your team with best practices for responsible AI development.
Case inspiration: AI that reduced cement emissions
A generative AI tool, Concrete.ai, designed millions of alternative cement recipes to lower production emissions.
Result: 30% smaller carbon footprint with optimized material use.
This is a real example of what AI can accomplish when applied thoughtfully with sustainability in mind.
Is your AI sustainable (and cost-effective)?
Let’s start with a quick assessment. Book time with our experts and begin the journey towards more sustainable AI.