Abstract of the Offer
Quaisr, a UK-based technology company, offers its SimOps platform for industries such as aerospace, defence, pharmaceuticals, consumer packaged goods, and is looking to expand into the space sector. The platform delivers digital twin solutions, integrating simulations and AI for improved decision-making and efficiency. It can be used for process optimisation, predictive maintenance, and workflow automation. Quaisr seeks partnerships or licensing deals to support its expansion.
Description
The Quaisr technology is a comprehensive SimOps platform that enables the orchestration and management of hybrid workflows involving simulations and artificial intelligence (AI). It is designed to support complex decision-making processes by combining real-time and historical data, allowing users to simulate, predict, and optimise the performance of systems, assets, and processes. The platform connects various types of models—physics-based simulations, machine learning algorithms, and other AI techniques—enabling the creation of digital twins. These digital twins serve as virtual replicas of physical systems, allowing users to monitor, simulate, and predict outcomes under different conditions.
Functions:
- Simulation Integration: The platform supports the integration of multiple simulation models, whether they are commercially available or in-house developed. These models can simulate the behaviour of assets or processes in various environments and conditions.
- Data Management: It connects real-time and historical data streams, feeding these into simulation models to enhance predictive capabilities. This integration allows for continuous updates to the digital twin, ensuring an accurate reflection of the current state of the physical asset or system.
- Machine Learning Integration: The platform also integrates machine learning algorithms that can learn from data to improve model accuracy and automate decision-making. These models are useful in scenarios where traditional simulation methods may not suffice due to limited data or complex interactions.
- Workflow Orchestration: The technology offers the ability to create, manage, and automate workflows involving simulations and AI. Users can define step-by-step processes to execute simulations, collect data, run predictive models, and generate insights.
- Customisation & Modularity: It provides modular, plug-and-play components that users can combine to create tailored workflows. This flexibility allows the platform to be applied across different industries and use cases.
- No-Code Interfaces: Non-specialist users can develop and deploy applications using the platform’s no-code tools, enabling broader accessibility across an organisation. This fosters collaboration among teams with diverse expertise.
- Versioning & Audit Trails: It tracks and versions every model, workflow, and dataset involved in the decision-making process, maintaining a detailed audit trail for transparency and compliance purposes.
Enabling Technical Concept
The core technical concept is the combination of AI and simulation technologies to create digital twins. A digital twin is a dynamic model that mirrors the behaviour of a real-world system by continually updating itself with real-time data and applying various simulation models to predict outcomes. This technology relies on a robust framework that integrates data ingestion pipelines, machine learning algorithms, simulation engines, and orchestration tools.
The platform ensures smooth interaction between these components, allowing for continuous optimisation, scenario analysis, and predictive insights. Machine learning enhances the performance of traditional simulations by learning patterns from historical data and improving the model's ability to predict outcomes under uncertain conditions. Simulations, on the other hand, provide deep domain-specific insights that are grounded in the laws of physics, offering high levels of accuracy. Together, these methods enable more holistic decision-making.
Potential Applications in the Space Sector:
- Spacecraft and Satellite Operations: The platform can create digital twins of spacecraft, satellites, or satellite constellations to simulate their performance under varying environmental conditions such as microgravity, radiation, or orbital debris. This enables space organisations to predict and prevent potential failures, optimise satellite positioning, and ensure mission success through real-time monitoring and predictive maintenance.
- Mission Planning and Execution: By leveraging simulations and real-time data, the platform helps in mission planning for space exploration, enabling engineers and mission control teams to model different scenarios. Whether it's for launching a spacecraft, planning a landing on a planetary surface, or managing deep-space communications, the platform allows for detailed simulations to anticipate challenges, improve mission outcomes, and reduce risks.
- Spacecraft Design and Testing: The platform allows aerospace engineers to simulate the design and operation of spacecraft systems before physical prototypes are built. Engineers can explore different configurations, simulate component wear and tear, and optimise systems for the unique environment of space, reducing the need for costly physical testing.
- Satellite Constellation Optimisation: Managing satellite constellations, such as those used in communication or Earth observation, is complex. The platform can simulate satellite orbits, potential interference patterns, and network traffic, helping operators optimise the performance of the constellation for maximising coverage, reducing latency, and maintaining system integrity.
- Space Station and Habitat Management: The platform can be used to model and optimise life support systems, energy use, and resource allocation in space stations or extraterrestrial habitats. By creating digital twins of these systems, operators can ensure that conditions remain optimal for human occupation, while also simulating future challenges such as resource shortages or system failures.
- Space Resource Utilisation: In the future, as space mining and resource utilisation become more viable, this technology can help simulate the extraction and processing of resources from asteroids or other celestial bodies. By running simulations, teams can assess feasibility, operational risks, and environmental impacts, enabling better decision-making for sustainable space operations.
Advantages and Innovations
This technology innovates by seamlessly integrating simulations with AI to form digital twins, providing a hybrid approach that bridges traditional physics-based models with machine learning. Unlike purely simulation-based systems, it continuously incorporates real-time and historical data to refine predictions, offering greater accuracy and adaptability. Its modular design allows users to build workflows tailored to space operations, such as spacecraft management and mission planning.
The platform supports no-code interfaces, enabling non-experts to use advanced modelling tools and reducing the need for specialized teams. Compared to traditional methods, it reduces development time by up to 50% and lowers operational costs through predictive maintenance and optimization. The inclusion of audit trails ensures safety and regulatory compliance, while its cloud-based architecture provides scalability without the need for additional infrastructure. This results in a versatile, cost-efficient solution for space operations.