Introduction
For decades, engineering teams have relied on simulation to accelerate research and development.
In principle, simulation should reduce the need for costly prototypes, shorten design cycles, and enable more confident decision-making.
In practice, however, many organizations find that their simulation tools create bottlenecks instead of removing them.
Traditional desktop-based solutions often require weeks of computation time to produce results, slowing down testing and forcing engineers to work around long delays.
Even when time is available, license and hardware restrictions frequently limit how many engineers can run simulations in parallel, leaving valuable resources idle while they wait for access.
This situation means that only a fraction of possible design variations ever get tested.
Engineers often have to prioritize a handful of “most likely” options, leaving many potentially better designs unexplored.
To make matters worse, model simplifications are often introduced as necessary means to speed up runtimes, which can reduce accuracy and increase the risk of design flaws later in the process.
The outcome is predictable: slower product development, less innovation, and more uncertainty in decision-making.
Instead of being a powerful enabler, simulation is too often reduced to a validation step at the end of the R&D process, rather than a driver of exploration and discovery from the very beginning.
Removing simulation bottlenecks in R&D
Quanscient Allsolve was created to change the role of simulation in R&D.
Instead of being a slow, license-limited tool that engineers turn to at the end of a process, it is designed to make simulation a central driver of exploration from the very beginning.
By running on the cloud, Allsolve removes the traditional barriers of hardware capacity and seat licensing.
Engineers no longer have to wait for resources or scale down their models to fit a workstation. They can run as many simulations as they need, in parallel, without compromising on accuracy.
This change has two main consequences.
- Speed: Runtimes that previously took weeks are reduced to hours, enabling teams to run a full set of design variations in a single working day.
- Scale: Instead of testing ten or twenty configurations, organizations can now examine thousands.
This combination of speed and scale means that simulation shifts from validating a few ideas at the end of the cycle to guiding the entire R&D process from the start.
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Allsolve extends the value of legacy tools by adding large-scale exploration on top of daily validation.
Decisions are based on far broader evidence, risks are reduced, and opportunities that would otherwise remain hidden can be uncovered.
Case examples
The impact of this shift is best seen in real-world examples.
Consider a typical design sweep: an engineer wants to test 100 variations of a component, and each run with a traditional desktop solver takes eight hours.
Sequential processing would mean 800 hours of computation — nearly five weeks before the results are available.
With Quanscient Allsolve, the same sweep can be completed in roughly eight hours by distributing all 100 runs in parallel across the cloud.
| Economic factor | Traditional desktop tools | Quanscient Allsolve |
|---|---|---|
| Design of Experiments | Sequential processing (100 variations @ 8 hrs = 800 hrs / 5 weeks) |
Massive parallel processing |
| Iteration capacity | Ex: 10-50 design iterations (limited by license availability) |
Ex: 1,000-10,000 iterations |
| Concurrent simulations | Limited ability to run concurrent simulations |
Hundreds of concurrent simulations |
| Optimization studies | Weeks-to-months for comprehensive studies (cost constraints limit exploration) |
Same-day comprehensive analysis |
The difference is not just in speed but in how engineers approach the problem.
Instead of narrowing down to a handful of likely candidates, they can afford to explore the full design space and make decisions backed by far richer data.
This is already visible in practice, with companies and research institutions applying Allsolve to change how they work.
- Infineon Technologies has run MEMS simulations at a scale that would be impractical with other tools.
- kiutra (case study) has reduced the need for multiple hardware prototypes, saving both time and development costs.
- Proxima Fusion, working on one of the most complex challenges in energy, has been able to complete advanced simulations in less than a day where older tools would have required a week.
These cases show a consistent pattern: the shift from slow, sequential workflows to high-throughput exploration.
Instead of waiting weeks for limited results, teams can now run comprehensive studies in a day, test thousands of possibilities, and move forward with greater confidence.
Simulation is no longer the final check at the end of R&D but the foundation of faster, more reliable innovation.
Practical applications
The strength of Quanscient Allsolve is not confined to one niche.
Its ability to handle strongly coupled multiphysics problems at scale makes it relevant across a wide range of industries.
In superconductors, it enables detailed 3D analyses that are vital for advancing technologies such as fusion energy, high-temperature superconducting cables, particle accelerators, and maglev transportation.
These are areas where accuracy and scale are critical, and traditional tools struggle to keep pace.
In the field of MEMS devices, Allsolve supports the design of components like ultrasound transducers, microspeakers, inertial sensors, and RF MEMS.
These devices are often highly sensitive to design variations, meaning that a larger number of simulations directly translates into better performance and reliability.
By making it possible to run thousands of design iterations quickly, Allsolve allows engineers to move beyond guesswork and develop products that are thoroughly optimized before they reach the prototype stage.
The platform also addresses key challenges in electromagnetics and RF design.
Antennas, electric motors, waveguides, and power electronics all benefit from the ability to simulate complex interactions across multiple physical domains.
With cloud scalability, engineers can explore different geometries and operating conditions in parallel, shortening the path from concept to validated design.
Finally, Allsolve is increasingly being used to support artificial intelligence in engineering.
Training surrogate models requires vast amounts of simulation data, often tens of thousands of runs. By making these datasets practical to generate, Allsolve helps bridge the gap between traditional multiphysics simulation and modern AI methods.
This creates opportunities not just for faster product development today, but also for more intelligent design processes in the future.
Implications for business leaders
For business leaders, the shift that Quanscient Allsolve enables is not just a technical upgrade — it has direct strategic implications.
When simulations run in hours rather than weeks, product development cycles accelerate.
Teams can make decisions earlier and with more confidence, reducing both time-to-market and the risk of late-stage design flaws.
This acceleration translates into a faster response to market demands and new opportunities, something every executive values in a competitive landscape.
Scalability also changes the economics of innovation. Instead of being constrained by hardware capacity or license costs, organizations can explore thousands of design variations at a fraction of the previous effort.
This means that resources can be allocated more efficiently: engineers spend less time waiting and more time designing, and companies avoid the hidden costs of prolonged R&D cycles and excessive prototyping.
Perhaps most importantly, treating simulation as a driver rather than a bottleneck allows companies to build a more resilient innovation pipeline.
Leaders gain greater visibility into risks and opportunities, while engineering teams can move faster without being limited by outdated workflows.
In practical terms, this creates a stronger competitive position — one built on faster learning, better products, and a culture of innovation supported by the right tools.
Conclusion
Instead of being used mainly to validate designs at the end, simulation can now guide exploration from the start of development.
Quanscient Allsolve makes this possible by removing the traditional barriers of speed, scalability, and licensing.
Runtimes that used to stretch into weeks can be reduced to hours, and design studies that once covered a handful of variations can now extend to thousands.
For leaders, the message is straightforward: faster cycles, broader exploration, and more efficient use of resources mean stronger products and a more competitive business.
By removing bottlenecks in simulation, organizations not only reduce costs and risks but also open the door to design options that would otherwise go unexplored.
Companies across industries from superconductors to microelectronics are already using this approach.
The choice for others is whether to continue working within the old constraints or adopt tools that remove them.
Quanscient Allsolve provides that option today.
