“Geminus AI brings Super-intelligence to solve problems that are too complex for humans” – Greg Fallon
Greg Fallon, CEO of Geminus AI, a veteran at the intersection of AI and industrial operations, says the energy sector solution lies in intelligence, not only infrastructure. By combining physics-based modeling with advanced AI, operators can now simulate system behavior in real time to pinpoint inefficiencies, adjust pressure settings, and prevent methane release before it happens, all while optimizing energy use and cost.
In this interview with The Energy Republic, Fallon provides insight into Geminus AI’s pivotal role in the energy sector, with emphasis on how the AI helps operators boost operational excellence across the energy value chain. Excerpt:
Interview By: Ndubuisi Micheal Obineme
TER: Could you provide an overview of the Geminus AI solution, including its performance metrics and your company’s value propositions for the energy, oil, and gas sector?
Fallon: Geminus AI provides AI assistants that help operators of mission-critical energy infrastructure improve performance and efficiency. These assistants analyze the real-time state of a system—such as a network of wells—and recommend control adjustments that achieve the operator’s goals. For example, if an operator wants to reduce water cut, Geminus can evaluate the system and, within seconds, suggest the optimal electric submersible pump (ESP) settings. Our deployments deliver an average production increase of about 10%—without any equipment changes or capital spending.
TER: Which specific role does the Geminus AI play in solving complex issues across the energy sector?
Fallon: We are bringing engineering superintelligence to the energy sector—helping companies solve problems that are too complex for humans or traditional simulators to handle in a practical timeframe. The result is more profitable operations that are almost always more efficient, using less energy to achieve the same output with fewer emissions.
Today, our focus is on helping companies both access and scale the capabilities of highly trained engineers. Our technology is advancing rapidly: we are continuously expanding the range of use cases we can support and making the system faster and easier to deploy.
TER: How does the Geminus AI help with prevention, detection, monitoring, and reporting methane emissions? What makes your company’s AI platform a unique solution for decarbonization, especially on critical infrastructures in the energy sector?
Fallon: Geminus stands out for the reliability and precision of its recommendations. Our “physics-native” models are grounded in the actual physical principles that govern each system, providing trustworthy guidance even when data is noisy or limited. We deploy rapidly—often within weeks—and can scale across complex asset networks of virtually any size.
Because our platform is built on generalizable scientific principles, it applies to almost any physics-driven process. The same core technology can support a high–water-cut well network one day and a utility grid the next. While this may sound like science fiction, it is the product of more than a decade of research and the latest advances in scientific AI—and our results consistently validate the approach.
These strengths translate directly into methane and flare reduction.
Rather than simply detecting leaks, the platform identifies the operating conditions, instabilities, and system behaviors that commonly lead to flaring or elevated emissions. By highlighting abnormal pressure, flow, or performance patterns early, Geminus enables operators to correct issues before they escalate. This improves situational awareness, sharpens root-cause diagnosis, and reduces false or unnecessary alarms that typically slow methane-management efforts.
The same capabilities support broader decarbonization goals.
By improving efficiency, preventing failure conditions, and reducing unnecessary energy consumption, Geminus helps operators cut emissions at the source—while maintaining reliability and overall energy security.
TER: How can the Geminus AI be integrated into existing assets, facilities, and industrial plants to boost operational excellence?
Fallon: Geminus integrates directly with an operator’s existing sensors, historians, SCADA systems, and simulation tools, so deployment does not require new hardware or major system changes. The platform uses this data to build a physics-native model of the asset or network and runs it in real time, giving operators fast, actionable insight for day-to-day operations.
A clear example is our flaring reduction work in the Bakken. By modeling the true physical behavior of the gathering system, Geminus gave the operator early visibility into the conditions that lead to flaring. This enabled proactive adjustments to routing and pressure, stabilizing the network and nearly eliminating routine burn-off.
This outcome reflects the broader value of Geminus. By delivering real-time, physics-based intelligence on top of existing infrastructure, we help operators run more efficient, predictable, and lower-emissions operations while strengthening the reliability of critical energy systems.
TER: How sustainable is the Geminus AI solution compared to other AI technologies in the global market? What makes your company’s AI technology unique, especially in this era of artificial intelligence?
Fallon: Geminus is sustainable by design. The platform helps operators run cleaner, more efficient systems while avoiding the heavy data and compute demands typical of many AI technologies. Because our models are grounded in physics, they do not require massive datasets to learn system behavior. This makes them compact, fast, and far less energy-intensive to train and deploy—and it also makes them more trustworthy.
Engineers can see how the models were built and validated, and they can understand why a recommendation was made. This stands in contrast to traditional AI, such as large language models, which rely on vast unstructured datasets which can generate errors and offer little transparency into how a specific answer was derived.
The physics-based approach also makes the technology more stable over time. Instead of rebuilding models when conditions change, Geminus updates them with targeted data, reducing retraining cycles and lowering both environmental and operational costs.
Geminus is purpose-built for high-consequence industrial environments. While much of today’s AI focuses on text, media, or general analytics, Geminus is designed for systems where physical accuracy is essential. By combining physics, machine learning, and fast computing, the platform delivers real-time intelligence that predicts outcomes, optimizes performance, and supports critical decisions. This blend of fidelity, speed, and efficiency makes Geminus uniquely suited for the next phase of AI in energy and heavy industry.
Together, these strengths enable operators to lower emissions while keeping the environmental footprint of the AI itself minimal.
TER: What strategic partnerships has your company established with its AI solution? Who are your clients in the energy sector, and how is the Geminus AI platform used in their operations to solve complex issues across?
Fallon: Geminus partners with operators and service providers who prioritize operational excellence, decarbonization, and energy security. One of our key partnerships is with SLB, a global technology leader in energy innovation, where we collaborate to bring physics-informed AI into mainstream oil and gas workflows. This expands access to real-time intelligence built on physical accuracy and strengthens the impact of our technology across the sector.
We work with upstream, midstream, and integrated operators across North America and the Middle East. Our customers include the largest super majors and many smaller operators.
Our customers use Geminus to stabilize and optimize gathering networks, improve water and disposal systems, reduce flaring, support emissions monitoring, and increase the predictability of high-consequence operations. The platform also helps teams forecast system behavior under changing conditions and optimize interconnected assets at the network scale.
Across the value chain, operators rely on Geminus to make faster, more confident decisions that improve performance, lower environmental impact, and strengthen the resilience of critical infrastructure. By combining physics-based modeling with real-time computing, the platform delivers results that traditional AI and legacy engineering tools cannot match—supporting a more reliable and sustainable energy system.
TER: How would you evaluate the strategic role of Geminus AI in bridging the gap between industrial innovation and risk management?
Fallon: Geminus challenges the idea that operational performance, risk, and climate impact must be managed separately. In high-consequence energy systems, these pressures are inseparable, and the industry can no longer afford reactive decision-making.
Operators need intelligence that sees ahead, not just reports what has already happened.
Geminus provides that forward view. By understanding how systems behave under any condition, the platform helps operators prevent instability, anticipate risk, and avoid the operational failures that drive emissions and cost. This shifts the focus from mitigation to true control.
The result is a more modern operating model where innovation, reliability, and decarbonization move together instead of competing. Geminus is building the intelligence layer that makes this shift possible.
