Geminus
Revolutionize industry with physics-informed AI for rapid ROI.. [Contact for Pricing]
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What is Geminus?
Geminus is the world's first generative engineering platform that automatically integrates data, physics, and computation for autonomous control of complex cyber-physical systems. The platform uses physics-informed AI (PI-AI) to fuse physics-based modeling with deep learning, enabling real-time operational decisions in industrial settings. It creates dynamic system digital twins that execute 1000x faster than traditional simulation while requiring far less data than conventional AI approaches.
Key features include physics-informed AI models that integrate simulation with deep learning, dynamic system digital twins for real-time scenario exploration, Model Predictive Control for widespread automation, autonomous systems capabilities for self-optimization and self-healing, and uncertainty quantification for trustworthy recommendations. The platform can model extremely large enterprise-level systems with minimal compute resources and allows model updates in hours rather than months.
Geminus is designed for industrial enterprises in heavy industries including oil & gas, space, defense, semiconductors, utilities, renewable energy, chemicals, and water distribution. It serves data scientists, modeling engineers, and industrial operators who need to optimize complex systems, reduce energy consumption, increase production, and achieve faster ROI. The platform is available natively on cloud, edge, or on-premise with enterprise-grade security.
Geminus pricing
Pricing model: Freemium
Pricing details are not publicly disclosed on the Geminus website. The company operates on a SaaS enterprise model targeting industrial enterprises. Pricing likely varies based on scale of use, specific customer needs, and the industrial sector. The platform is available through SLB (Schlumberger) as a reseller partner for oil and gas companies. Contact information is provided for inquiries at [email protected]. The company has raised $25M+ in funding.
Geminus pros
- Physics-informed AI fuses physical laws with machine learning for accurate predictions
- Creates accurate models in hours compared to months for traditional AI
- Executes 1000x faster than traditional simulation
- Requires only sparse data instead of large volumes of high-quality real-world data
- Works effectively when real-world data is difficult to acquire or unavailable
- Enables real-time optimization of complex industrial systems
- Provides uncertainty quantification for trustworthy recommendations
- Supports Model Predictive Control for widespread automation
- Enables autonomous self-optimization and self-healing systems
- 40% reduction in energy usage demonstrated in water distribution networks
- 10%+ increases in oil production through pump optimization
- Eliminates flaring in natural gas networks by adjusting settings
- Models easily updated with new data points in a few hours
- Secure platform with data privacy - client data never used for external pre-training
- Available on cloud, edge, or on-premise for data sovereignty compliance
- Scales across largest enterprise-level industrial systems
- Reduces ROI timeline from years to weeks
- Combines synthetic data and measured data effectively
Geminus cons
- Pricing details not publicly available on website
- Primarily targets enterprise industrial customers, not small businesses
- Requires existing simulation models or internal modeling experience for best results
- Fully autonomous self-optimizing systems still emerging, most applications have humans in loop
- Focused on specific heavy industries, may not suit all sectors
- Pilot deployment requires 3 weeks to 3 months depending on scope
- Requires internal stakeholder time and willingness to support deployment
- Best suited for processes with clear controls that can be adjusted
Frequently asked questions about Geminus
What is physics-informed AI and how is it different from traditional AI?
Physics-informed AI (PI-AI) fuses established physical laws with machine learning by integrating physical laws such as conservation laws and differential equations within data-driven models. This ensures predictions remain consistent with known scientific principles. Unlike traditional data-hungry AI, PI-AI enhances model accuracy, reduces the need for extensive datasets, and improves generalizability, particularly in complex systems where data may be sparse or noisy. Physics and data serve to regularize each other - data ensures physics model predictions don't drift from reality while physics ensures predictions don't overfit data.
How fast does Geminus create models compared to traditional approaches?
Geminus creates accurate models in hours compared to months for traditional AI. The platform's models execute 1000x faster than traditional simulation. When equipment or process specifics change, a production company can update the model in a few hours rather than the lengthy retraining cycles required by conventional AI techniques.
What industries does Geminus serve?
Geminus primarily serves heavy industries including oil & gas, space, defense, semiconductors, utilities, renewable energy, chemicals, materials, water distribution, and electricity distribution and storage. The platform is designed for industrial enterprises seeking to optimize complex systems in these sectors, with specific applications for energy networks, flow assurance in oil & gas, and process optimization.
How much data does Geminus require to train models?
Geminus requires only sparse data compared to traditional AI methods. The platform combines synthetic data and measured data to create powerful, explainable machine learning models when large volumes of high-quality real-world data aren't available or are difficult to acquire. Models leverage system physics for training, resulting in more accurate predictions with far less data than typically needed.
What are the real-world results customers have achieved?
Geminus customers have achieved significant measurable results: 40% reduction of energy usage in water distribution networks, eliminated flaring in natural gas networks by adjusting network settings for overpressures, and 10% increase in oil production by optimizing submersible pump control across well networks (resulting in tens of millions of dollars in value across six wells in a single day). Production increases of a few percent to over 10% are often achieved across various applications.
Is Geminus secure and where can it be deployed?
Geminus offers a full stack solution available natively on cloud, edge, or on-premise, with significant focus on navigating industrial sector security and data sovereignty challenges. The platform trains models securely on a combination of customer data, sensor readings, and simulation models. Data privacy is ensured - client data is never used to pre-train models shared outside a customer's environment.
What is a dynamic system digital twin and how does it work?
A dynamic system digital twin is a physics-based model that enables real-time scenario exploration and what-if analysis. The platform starts most implementations with a physics-based digital twin, offering a path to model predictive control and eventually autonomous self-optimizing and self-healing systems. Users can explore scenarios and receive control recommendations, with the system able to automate controls to make timely process corrections without as much human intervention.
How does Geminus compare to traditional simulation?
Traditional physics-based simulations are predictive but demand deep domain expertise and are computationally intensive, limiting their use for real-time operational decisions. Geminus overcomes this by fusing adaptive AI with physics using multi-fidelity modeling, enabling predictive models that combine high accuracy and speed with fast updating and quantified uncertainty. The resulting models execute 1000x faster than simulation while maintaining physics-based accuracy.
What is the typical deployment timeline for Geminus?
A pilot deployment requires anywhere between 3 weeks and 3 months, depending on scope and availability of internal data and support. The approach significantly reduces time to ROI from years to weeks. For companies new to PI-AI, selecting the right application for the first deployment is important - ideally a process with meaningful financial impact, clear controls, and observable output changes.
Who are Geminus's key team members and partners?
Geminus's technical leaders include Chief Scientist Karthik Durasamy, who specializes in scientific foundational models, and Chief AI Scientist Alex Gorodetsky, an expert in computational autonomy with a PhD from MIT. CEO Greg Fallon leads the company. Geminus has a strong reseller partner in SLB (formerly Schlumberger), one of the most trusted companies in energy-related industries, which helps gain traction with oil and gas companies. The company has raised $25M+ in funding.