Leading the Future of Sustainability: An In-Depth Look at pacemaker.ai Carbon Intelligence Solutions with David Horsch

David Horsch is an environmental engineer by profession, with a background in advising medium-sized enterprises on environmental management post-graduation. His work has particularly revolved around greenhouse gas emissions, a critical aspect of climate change mitigation. Over the past four years, he has delved deeply into CO2 accounting and reduction, identifying a growing demand for digital solutions in this field. Leveraging his expertise, David Horsch now plays a pivotal role at pacemaker.ai, contributing his wealth of knowledge to the development and deployment of Carbon Intelligence solutions.

What can be understood by “Carbon Intelligence” in practice?

David explains, "Intelligence stands for 'insight' or 'intelligence.' It’s about giving users deep insights into their CO2 emissions. For this, many data points need to be intelligently connected, and emissions must be automatically calculated." He adds, "From this, we create a Product Carbon Footprint that provides insight into which component or process offers the greatest emission reduction potential. From this, we can derive which department in the company should act, such as procurement or production, to reduce emissions."

What are typical use cases of Carbon Intelligence among your customers?

"In most cases," says David, "our customers are asked by their customers to provide the CO2 footprint of the delivered products. For most industrial companies, the largest emission sources are in the supply chain. Therefore, they want to receive accurate information and compare various suppliers regarding CO2 to improve their balance. Regulatory requirements for product declarations are also emerging on the horizon, which is why our customers want to prepare early."

When is it worthwhile to think about such software solutions? What are the typical costs?

He mentions, "It is worthwhile even for customers with a few products. For example, the company produces 20 different types of screws at 8 locations worldwide with different suppliers. It quickly becomes clear: a minimum of 160 PCFs must be calculated. Using Excel and consultants for this is expensive and not scalable, especially since the values need to be recalculated annually or project-specific. On average, creating a PCF often costs several thousand euros per product."

"Our PCI solution," David continues, "starts at 1000€ per month with a specific quota of included PCFs. This includes all costs such as cloud costs, databases, user management, and of course access to continuously released new features."

How flexible are such systems? Can they handle every type of company and process?

David states, "We focus our solution on the manufacturing industry, as our technology can quickly read and analyze product BOMs."

"Technically," he adds, "we are very flexible in terms of system integration. With customized data interfaces, we can target and use almost any system."

What application areas can potentially arise in the future?

"In the future," Horsch notes, "we want to provide users with concrete recommendations on actions to take to reduce emissions, alongside the results. The potential CO2 savings and especially the cost aspect play a crucial role."

"Additionally," he continues, "digital data spaces will gain importance in the future for exchanging data across the supply chain. We want to connect PCI with the Catena-X network to enable automotive customers to exchange CO2 data securely and efficiently. This will provide an even more realistic picture of CO2 emissions in the supply chain." He also mentions, "Lastly, CO2 is just one dimension of sustainability. Customers also want to collect, report, and improve other sustainability data."

What role does AI play in relation to “Carbon Intelligence”?

David explains, "We currently use AI in the calculation of the CO2 value. Emission factors are used for this. Our AI automatically selects the most suitable emission factor for the user. Behind this is a language model that understands semantic relationships. If no suitable factor is available, the AI can break down a product in the BOM (e.g., sensor) into its individual parts and search the database again. This iterative process enables an accurate and efficient method to calculate CO2 emissions of products." He adds that AI can also help identify anomalies in the data and inform the user about them. "Language models," David continues, "can also be helpful in reading documents and receipts like electricity bills to avoid manual entries. Language models like GPT-4 handle this surprisingly well."

What prerequisites should a manufacturing company have if it is considering implementing Carbon Intelligence solutions?

"All we need is a digital BOM to get started. This is usually available in every company." "Based on this," David adds, "we can make the calculation more accurate or specific with additional customer data. This includes data such as supplier locations, goods receipts, and production data." "The advantage of our solution," he emphasizes, "is the speed and simplicity of operation. To get started, there’s no need for long project plans, lead times, or consulting days. For an initial pilot project, it takes only a few days from data provision to the first result. This creates immediate value for customers, who can then iteratively improve the results."

Looking ahead, pacemaker.ai is poised to lead the charge in sustainable industrial practices through its advanced Carbon Intelligence solutions. By harnessing the power of AI and real-time data integration, pacemaker not only empowers businesses to accurately measure and reduce their carbon footprints but also prepares them for the regulatory landscapes of tomorrow.