

Better forecasting increase the certainty when optimizing energy assets. Real-time data of markets, weather, and assets play a fundamental role to outperform competitor’s algorithms and trade on day ahead, intraday, EPEX, and more. With a best-in-class ML ops set up, energy companies roll-out multiple energy forecasting models for different time horizons, different markets, and different profile granularities.
Renewable assets are at the hearth of the energy transition, from EVs to batteries and from heat pumps to batteries. Monitoring and steering these assets is critical. Through forecasting, companies can optimize their asset for commercial value, asset lifetime, and up time. Predictive maintenance is key to keep asset lifetime and uptime high. And monetizing over multiple energy intensive assets is becoming a more complex game.
Grid congestion and price spikes create new revenue opportunities for flexible assets. We develop algorithms that optimize charging and discharging cycles based on real-time market signals. By automating flexibility, we turn technical constraints into scalable financial value.
The energy transition is an immense logistical challenge that outpaces manual human oversight. We deploy agentic AI to digitize expert knowledge and automate repetitive diagnostic workflows. This allows your engineering teams to solve complex grid problems instead of managing routine data entry.

Global trade relies on fragmented data across rail, barge, and road that often leads to inefficient routing. We replace manual guesswork with algorithmic precision to optimize payloads and reduce deadhead miles in real-time. Our solutions unify disparate data streams into a single source of truth, allowing logistics leaders to scale operations without increasing carbon overhead.
The shift to electric fleets introduces complex variables like charging windows, grid constraints, and battery degradation. We engineer the intelligence behind large-scale charging networks and shared mobility platforms to balance uptime with energy costs. By integrating real-time telemetry with predictive models, we ensure fleets remain operational, profitable, and fully decarbonized.
Developing the next generation of sustainable hardware requires rapid iteration that manual R&D cannot sustain. We build automated data pipelines that digitize physical experimentation and accelerate the discovery of high-performance prototypes. By leveraging predictive modeling and real-time monitoring, we help engineers identify winning materials months ahead of traditional laboratory schedules.

Vast planetary data is useless without the infrastructure to process it at scale. We build high-throughput pipelines that transform raw pixels from satellites, drones, and sensors into actionable spatial insights. Our systems enable organizations to monitor environmental changes and infrastructure health in real-time, moving from observation to immediate response.
Generic weather models often fail to capture the localized volatility that impacts specific assets and supply chains. We implement advanced AI and ML models that achieve superior accuracy in hyper-local forecasting and long-term climate risk assessment. These high-precision insights allow stakeholders to predict the unpredictable and build resilience into their operations against a changing climate.
Mapping complex material flows and environmental footprints manually is a bottleneck for the circular economy. We deploy LLM-powered systems that automate the ingestion of technical documentation to track material lifecycles and regulatory compliance. By digitizing this expertise, we help companies accelerate their transition toward Net Zero with transparent, data-backed sustainability reporting.

Industrial waste is often the result of delayed detection in production anomalies. We deploy real-time computer vision and ML models that monitor quality at the source to eliminate scrap and reduce energy intensity. By integrating predictive engines into supply chain dynamics, we help manufacturers anticipate maintenance needs and optimize resource allocation for a circular production cycle.
Aligning real estate portfolios with net-zero standards requires synthesizing massive volumes of disparate building data. We build Multi-Agent Systems and Digital Twins that simulate energy performance and identify the most impactful retrofit opportunities. These intelligent frameworks allow asset managers to assess efficiency at scale and execute decarbonization strategies with mathematical certainty.
Traditional hardware R&D is too slow to meet the urgent demands of the energy transition. We engineer automated data pipelines that digitize the entire experimentation process, significantly increasing the velocity of the feedback loop. By leveraging predictive modeling and real-time monitoring, we help scientists bypass dead-ends and identify high-performance, sustainable prototypes months ahead of schedule.