QuasiScience News Feed

Fashion Innovation in Florence

Fri, 15 Dec 2023 00:00:00 GMT

[Florence, IT] - [December 15, 2023] - QuasiScience, a growing Simulation and Data Science company, will be attending the first Demo day of the Italian Fashion-tech accelerator StyleIt.

Event Details

Dates: December 15, 2023
Location: Manifattura Tabacchi, Florence, ITA

About QuasiScience

QuasiScience mission is help businesses become more successful through the use of advanced Numerical Simulations and Data Science. With a team of talented industry experts, researchers, and developers, QuasiScience is pushing the boundaries of process optimisation through the use of Mathematics.

About StyleIt

StyleIt is the FashionTech accelerator of the CDP National Accelerator Network: an initiative of CDP Venture Capital SGR together with Startupbootcamp and GELLIFY.

Contacts

PR Team

Hackathon for the Creative Industries

Sat, 11 May 2024 23:00:00 GMT

[London, UK] - [May 12, 2024] - QuasiScience, a growing Simulation and Data Science company, is partnering with HOST at MediaCityUK to support a hackathon focused on developing innovative AI solutions for the creative industries.

This exciting event will bring together talented developers, designers, and creative thinkers from across the region to collaborate and develop novel AI applications for various creative fields. The hackathon will provide participants with access to QuasiScience expertise in developing custom machine learning implementations and deep industry knowledge from researchers at the University of Salford and MediaCityUK.

QuasiScience's CEO, Marco Ghilardi, expressed enthusiasm for the event, stating:

We are thrilled to partner with the HOST for this initiative. The focus on craftsmanship and control over the creative process has made the creative industries more resistant to the adoption of new technologies at scale. This hackathon will provide a platform for artists and innovators to co-develop solutions and test the boundaries of what's possible. We are excited to see the creativity and ingenuity that will emerge from this event.

Hackathon Details

Dates: May 15 and 16, 2024
Location: MediaCityUK, Salford, M50 2NT
Theme: AI for the Creative Industries

About QuasiScience

About HOST

HOST is Salford's Home Of Skills & Technology. It is an innovation hub at the heart of MediaCityUK and offers unique environment to learn, grow and succeed. HOST aims to bring companies and start ups under one roof to foster innovation and help individuals learn the skills to take on more technical roles.

About the University of Salford

The University of Salford is a renowned academic institution with the mission of broadening access to education. The University's Immersive Technology department is a leader in the field of AI research applied to media and production.

Contacts

PR Team

AI-driven Film Editing Software

Sun, 14 Jul 2024 23:00:00 GMT

[Bristol, UK] - [July 15, 2024] - QuasiScience, a growing Simulation and Data Science company, will be co-developing Virtuoso AI with Nulight Studios.

Virtuoso AI is a suite of powerful video editing tools that bridge the gap between generative AI and the film and television industry. Our MVP will automate the identification and replacement of unwanted objects in video: a time-consuming task for Visual Effects (VFX) Artists that often requires meticulous frame-by-frame work. By automating this process, filmmakers can produce high-quality content more efficiently, streamline post-production, and significantly reduce costs.

This tool will be particularly beneficial for natural history documentary makers in the South West, removing unwanted objects like lens dirt, car or town lights, and radio collars on animals to ensure pristine footage.

Key advantages of the AI-powered object-replacement tool include seamless integration with existing professional film editing and VFX software, support for industry workflows and file formats, and enhanced IP security.

This innovative tool will showcase the potential of the Virtuoso AI platform, opening new avenues for creativity and efficiency in film and television production and establishing Nulight as pioneers in AI-driven video editing.

Unlike other AI platforms, integrity and transparency is at the core of Virtuoso AI. The models are trained using legally sourced and licensed films, with a list of sources made publicly available. Additionally, the platform ensures customer footage cannot be collected or used for training.

About QuasiScience

About Nulight Studios

Nulight Studios is a leading UK provider of motion picture film scanning, restoration and digital remastering services to the broadcast and film distribution market.

Contacts

PR Team

London Defence Tech Hack 2025

Sat, 24 May 2025 23:00:00 GMT

[London, UK] - [March 25, 2025] - QuasiScience, a growing Simulation and Data Science company, attended the London Defence Tech Hack 2025, an event dedicated to exploring innovative solutions for the defence sector.

Event Details

Dates: May 17-18, 2025
Location: Royal Military Academy Sandhurst, UK

Program Overview

During the intense two-day event, over 250 of the best young engineers, start-ups and industry leaders descended on the Royal Military Academy Sandhurst for London Defence Tech Hackathon 2025 to solve real life defence problems from the UK UK Ministry of Defence. The hackathon will provide a platform for developers, designers, and industry experts to work together and learn from each other.

Outcomes

Statement from Marco Ghilardi, Managing Director of QuasiScience:

It was a deeply enriching experience to be part of the hackers team at the Royal Military Academy Sandhurst, an important centre for military training with great historical significance. The event gave us several insights into how technology can enhance operational capabilities. A big thank you to the personnel at Sandhurst for their hospitality and support, and to the sponsors and organisers for making this event possible.

About QuasiScience

QuasiScience mission is help businesses become more successful through the use of advanced Numerical Simulations, Automation, and Data Science. With a team of talented industry experts, researchers, and developers, QuasiScience is pushing the boundaries of process optimisation through the use of Mathematics.

Related Resources

Official Linkedin Page

Contacts

PR Team

Partnership with London Lounge Radio

Sun, 20 Apr 2025 23:00:00 GMT

[London, UK] - [March 31, 2025] - QuasiScience, a growing Simulation and Data Science company, is partnering with London Lounge Radio ("LLR"), an organisation specialising in the curation of exceptional musical events and art exhibitions.

This partnership will help both companies accelerate their grow through strategic resource and knowledge sharing. LLR core expertise lies in the creation of engagement and design of unique experiences; through our partnership they will be able to speed up the development of certain features of their platform. Meanwhile, QuasiScience will benefit from LLR expertise in audience engagement, allowing us to better connect with our users and clients.

Statement from Alexander Maffei, Director of London Lounge Radio:

Thanks to the team at QuasiScience we already feel more confident in creating more ambitious events and we will soon kick off the development of a new platform that will allow us to better connect with our audience. We are excited to work with QuasiScience and look forward to the innovative solutions we can create together.

Statement from Marco Ghilardi, Managing Director of QuasiScience:

We are excited to partner with London Lounge Radio, an organisation that shares our passion for innovation and creativity. This partnership will allow us to apply our expertise in data science to help LLR create even more engaging and memorable experiences.

About QuasiScience

About London Lounge Radio

London Lounge Radio is dedicated to fostering spaces where up-and-coming musicians and artists can connect with audiences looking for authentic, innovative experiences.

Related Resources

Official Instagram Page
Next LLR Event

Contacts

PR Team

Riding the Digital Dragon

Sat, 04 Oct 2025 23:00:00 GMT

A few weeks ago, the Chinese State Council launched a new plan to embed artificial intelligence throughout the Chinese economy - the AI+ initiative. AI+ is a national program that combines investment, procurement, regulation and local-level execution to make AI an engine of productivity throughout the vast country.

For businesses outside China, this development is more than a geopolitical headline. It is a masterclass in how to integrate AI technologies into operations quickly, strategically, and at scale, although the lesson also comes with some warnings.

This article distills the key points from China's AI+ strategy into actionable insights for Western businesses of every size and sector.

What is China's plan for AI?

Unlike China's previous AI strategies, the AI+ strategy focuses on the importance not of research, but of application. Rather than labs, Beijing is funding demand creation - making sure industries adopt AI to raise productivity and competitiveness across the country.

The strategy focuses on:

fiscal and procurement support to speed up enterprise trials and deployments
local and municipal pilot programs to create demand and data
workforce retraining
parallel governance and labelling regimes to control risk

It's a very applied, practical approach: for China, AI is a tool to raise national productivity, not an abstract technology. This builds on China's long-stated approach in documents such as the 2017 New Generation Artificial Intelligence Development Plan.

There are clear lessons here for Governments, but why should entrepreneurs care? Because the entire business landscape is shifting:

Your suppliers, competitors, or partners in China may soon run leaner, faster operations thanks to AI diffusion
Standards and practices from AI+ funded pilots will likely influence global norms
Most importantly, the processes China are using to diffuse AI across their nation (e.g. procurement design, pilot programs, modular adoption) can be adapted for digital business transformation, even in small businesses in non-technical industries

Five Lessons for Western businesses

The Chinese AI+ approach holds important lessons not only for transforming a vast nation's economy, but also for integrating AI into any business, large or small. Here are the top five:

1. Engineer demand, don't wait for it

China isn't waiting for AI adoption to occur 'naturally' as a result of market forces. Through subsidies, municipal pilot projects, and state procurement, it is creating early demand to fuel iteration.

Lesson for business leaders:

For rapid transformation, don't passively wait for specialist staff or customers to demand AI or digital tools. Instead:

Fund internal pilots with clear KPIs
Offer incentives for business units that adopt new systems that generate proven results
Commission expert reviewers to identify where your business could genuinely benefit from AI

2. Think local and modular, not business-wide

China's central government is setting the national direction, but it is provinces and cities that are running pilots — healthcare in one city, smart grids in another.

Lesson for business leaders:

Instead of chasing a massive, enterprise-wide digital transformation (which is often expensive and unwieldy), start with modular pilots in departments or regions. A retail company, for example, could test AI-driven offline demand forecasting in one city before rolling it out nationally.

3. Adoption is not just about tech

China's AI+ aligns investment, talent training, and standards-setting, recognising that AI adoption requires more than just good technology. Great technologies can be useless if they are not properly integrated or understood, or if they are used irresponsibly.

Lesson for business leaders:

Digital tools fail when you just buy software without also preparing people and processes. Think about:

Budgeting for integration, not just licenses
Retraining staff to use new systems
Creating company-wide standards for responsible use, data governance, and safety

4. Ignore the hype

China's rhetoric around AI+ emphasises efficiency gains, for example in factory yields, the accuracy of medical diagnostics, or the optimisation of logistics. Conversely, it is not interested in abstract 'breakthroughs' or soundbite worthy 'transformations'.

Lesson for business leaders:

Whilst few industries have as much hype around them as AI does right now, businesses, like Governments, must learn to ignore it. Instead, focus ruthlessly on ROI. Hire qualified deep technical experts to identify where AI can make a real difference in your business, or start by asking yourself:

Where your teams spend a lot of time on repetitive tasks
Where you need to influence the behaviour of large numbers of customers
Where you need to make difficult decisions based on uncertain or unreliable data
Where you need to safely and reliably organise large amounts of data
Where you need to create high quality content quickly
Where your teams spend a lot of time staying on top of trends or developments
Where you need to test lots of different products/approaches

Begin where measurable efficiency gains are likely, not where hype is loudest

5. Build (the right) partnerships

China's big tech companies (Alibaba, Baidu, Huawei, etc.) are scaling AI investment in parallel with the state, creating an effective ecosystem in which AI technologies can develop rapidly.

Lesson for business leaders:

You don't need to build everything in-house. Many of the most exciting AI technologies require deep expert, multi-disciplinary teams to implement them effectively, so it is crucial to find the right partners. This includes:

Expert developers who can not only build software or sell you SaaS products, but also integrate and customise tools so they work for your business
Traditional engineers or specialists who understand the systems you are trying to model, automate or modernise
Training providers who can upskill your teams
Regulatory and standards experts who can ensure you are not just compliant, but responsible
Deep technical consultants who can give you an honest appraisal of where AI will actually add value, not just make a nice headline

Risks to Keep in Mind

China's AI+ strategy also brings to mind three key risks that business leaders face when implementing AI technologies.

1. Beware of access challenges in regulated markets

China's plan includes governance and labelling regimes that will affect what data and systems can be used.

Lesson for business leaders:

For Western firms, localisation (data residency, local legal constructs, compliant model governance) will also be required in many markets - make sure you have processes for safe data handling, partnerships, and any joint ventures.

2. Cultural resistance is real

Here's one way in which businesses need to act very differently from states: while China is able to issue top-down mandates for AI adoption, Western companies need buy-in from employees and customers.

Lesson for business leaders:

Adoption plans must include change management and the creation of good governance to build trust.

3. Hasty adoption can be wasteful

Last but not least, it's important to note that some Chinese pilots have succeeded because of political backing, not economics. In business, we don't have this luxury.

Lesson for business leaders:

Insist on evidence-based models of likely ROI before investing in projects.

Riding the Digital Dragon

As the old Chinese adage goes: if you ignore the dragon, it will eat you; if you try to confront it, it will overpower you; if you ride it, you will take advantage of its might.

The mighty dragon that is AI+ reminds us that, while the winners in this new business era definitely won't be those who ignore AI, they also won't be leaders who just try to outcompete everyone else for the smartest tech. The key to success is making AI adoption easy, fast, and scalable.

For Western business leaders, the lessons are clear:

Engineer demand, don't wait for it
Think local and modular, not business-wide
Adoption is not just about tech
Ignore the hype
Build (the right) partnerships
Beware of access challenges in regulated markets
Cultural resistance is real
Hasty adoption can be wasteful

As part of the right strategic approach, AI tools can bring about a positive transformation in almost any business. Contact QuasiScience today if you'd like to discuss how to get the most out of these exciting new technologies.

References

China State Council release on AI+ initiative (State Council guideline, Aug 27, 2025). (State Council of China)
Stanford DigiChina translation: "A New Generation Artificial Intelligence Development Plan" (2017) — foundational planning document that frames Beijing's AI goals through 2030. (Stanford.edu)
RAND analysis: "Full Stack: China's Evolving Industrial Policy for AI" — analysis of China's use of industrial policy across the AI stack. (RAND Corporation)
Carnegie Endowment: commentary on China's intent to diffuse AI across the economy and challenges of large-scale integration. (Carnegie Endowment)
Reporting on corporate investment: Alibaba's expanded AI spending and strategic posture (August-September 2025 reporting). (Investopedia)

Digital Twins for Leaders

Thu, 09 Oct 2025 23:00:00 GMT

Digital twins - mathematical models of physical systems - are one of the most powerful emerging AI technologies. By enabling organisations to simulate complex products or processes before committing the resources required to make real-world prototypes, digital twins can cut costs, accelerate innovation, and reduce environmental impacts. They have applications in almost every field (from engineering and energy to pharmaceuticals and healthcare). However, successful deployment requires not only strong technical teams, but also leaders who take the right strategic approach.

What is a Digital Twin?

Digital twins can be very simple. For example, a single computer chip linked to a sensor in a pipe, running an equation to set off an alert when a water tank is about to overflow, is a basic digital twin. It uses data and a mathematical model to simulate what is going on in the tank and visualise this for users.

Although many digital twins are far more complex than this, they all have the same four elements:

Data: collected from sensors (e.g. the one in the pipe) or pre-existing datasets (e.g. the dimensions of the tank).
Model: equations or algorithms that express the relationship between inputs and results (e.g. the equation linking the volume of the tank and the rate of water inflow to when it will overflow).
Simulation: a computational platform continuously using sensor data to make a prediction using the model (e.g. the software that runs the equation and sets off an alert when the tank is nearly full).
Visualisation: Dashboards and interfaces that allow engineers, managers, or decision-makers to understand the results of the twin (e.g. the screen showing the alert)

Competitive Advantage in the Age of AI

This simple concept gives rise to exciting use cases across almost every major sector of the economy. Leaders who have mastered the art of leveraging digital twins are already gaining a significant competitive advantage.

To give just a few examples:

For healthcare, Pfizer are using digital twins to predict how compounds will interact with biological systems, reducing reliance on animal testing. Meanwhile, Philips are developing patient-specific digital twins of hearts that let cardiologists simulate treatment outcomes and personalise care plans.
In the energy sector, Siemens are using digital twins for predictive maintenance of power plants, reducing downtime by 10%; they are also using this technology to optimise placement of wind turbines and increase yield.
Turning to aerospace, NASA are simulating deep space missions using digital twins, to anticipate mission failures and ensure that expensive equipment is not wasted.
When it comes to the automotive sector, BMW are running digital twins of automated production lines, significantly reducing planning time; while Volvo are developing vehicle twins to monitor fleet performance and facilitate remote updates.
Digital twins are big in logistics: DHL are running warehouse twins to optimise the routes of robots and the layout of shelves, improving picking efficiency; while Maersk are building digital twins of global shipping routes to improve their resilience in the face of disruption.
In retail, Walmart are using digital twins of shops and refrigeration units to reduce emergency maintenance by 30%.
And digital twins are even improving our cities: Singapore's Virtual Singapore project is a full-scale city twin for testing traffic flow, energy demand, and disaster response; while Helsinki have also created a city twin to model noise, pollution, and building energy consumption.

Five Secrets Developers Wish Leaders Knew

Despite their enormous potential, poorly implemented digital twins can be expensive and ineffective. Even small digital twin pilots require significant investments in talent, data and architecture, so it is crucial to ensure projects are not derailed by the challenges that digital twin engineers face at every stage of the development process. So how can business leaders reap the benefits of digital twins, without risking expensive project failures?

1. Ask the Right Questions

Returns on investment in digital twin infrastructure are significantly more likely if leaders start with the right questions. Leaders keen to explore digital twins but unsure where to start should ask themselves:

Where in my organisation are we spending a lot of time or money on prototyping or testing products or processes?
Where are we under pressure to reduce waste or emissions?
Where is the length of our innovation cycle holding us back?
Where are we managing risks that arise from complex systems e.g markets, supply chains, or regulatory environments?
Where are our operations subject to a high failure rate e.g equipment failure or missed deliveries?

Answers to these questions will give a good indication of where digital twins could generate value for your business, as a foundation for a discussion with expert engineers.

2. Demand effective integration

Unfortunately, many digital twin projects fall down not because of engineering flaws in the twins themselves, but because of a failure to integrate with the company's wider systems. A twin that cannot exchange data with Enterprise Resource Planning, supply chain platforms, Internet of Things sensors, or design software, risks becoming just an expensive visualisation tool. Leaders should insist on interoperability from the outset, ensuring that twins can both consume and generate data across the enterprise. This means aligning digital twin initiatives with existing data strategies, cloud infrastructure, and integration standards, so the insights generated can actually drive decisions and actions.

3. Invest in security

In their enthusiasm to implement exciting new technologies, technical teams sometimes forget that digital twins often bring together sensitive operational, financial, and even personal data, making them an attractive target for cyberattacks.

As always, breaches are a major business risk. Therefore, it's essential that leaders treat digital twin environments as critical infrastructure, embedding cybersecurity controls such as identity management, encryption, and network segmentation from the outset, and committing to regular testing and monitoring in alignment with GDPR.

4. Ensure Stakeholder Buy-in

Surprisingly, the most common reason digital twins fail is not technical. Instead, digital twin projects most often fall down due to a lack of trust from the users and the organisation more widely. Experienced employees have good reasons to distrust digital twins, worrying that they will degrade standards or create avoidable errors. For this reason, clear governance is crucial. From the outset, leaders should consider:

How clearly models express reasoning and uncertainty
Accountability for input data quality
Transparency about how digital twins work (they must never be black boxes)
A collaborative model-design process, bringing together both traditional engineers/experts and digital specialists
Through-life processes for assessing and validating twins.

5. Choose the Right Partners

Digital twins sit at the intersection of engineering, data, and operations, so multi-disciplinary teams are central to their success. Traditional engineers or specialists who understand the real-world systems being modelled, and data scientists who can model and analyse the systems, must work together closely. While the former group will often come from within your organisation, it can be challenging to choose the latter.

As a guideline, leaders should look for digital twin engineers who can solve problems at every stage of the digital twin engineering process. It is worth asking how they will address these issues:

Data must be effectively integrated from all the different sources involved; cleaned effectively when it may be unreliable or low quality; and kept secure. Systems must also be able to handle any latency (delay) if data is being collected in real time; and large volumes of data when necessary
Models should strike a balance between being complex enough to be useful but simple enough to work with limited processing power; draw on an advanced, inter-disciplinary understanding of the real-world system; evolve with the real-world system; and capture uncertainty
Simulations need to have sufficient computing power; synchronise effectively with live systems; and, where necessary, integrate multiple simulations (if the twin is for an entire system, rather than just one component)
Information should be displayed clearly and intuitively for non-technical users, and integrate with existing systems in the organisation

Tech Savvy Leaders Unlock Value

Digital twins are no longer a futuristic concept — they are a practical tool already reshaping industries, and giving leaders a powerful way to de-risk decisions by exploring possibilities and testing solutions before committing significant resources.

But, as ever, the technology itself is only part of the story. Leaders must ask the right questions, ensure integration with other systems, invest in adequate security, build trust through effective governance, and partner with the right mix of technical and domain experts. Organisations that approach digital twins as strategic capabilities — rather than isolated experiments — will gain the most from their deployment.

In short, digital twins offer a rare opportunity to cut costs, accelerate innovation, and reduce environmental impact all at the same time. Leaders who seize this opportunity with discipline, vision, and the right partners will not only future-proof their operations, but also gain a lasting competitive advantage.

Affordable Advanced Simulations

Mon, 13 Oct 2025 23:00:00 GMT

For decades, advanced simulations have been the preserve of aerospace giants, Formula One teams, and government laboratories equipped with multimillion-dollar supercomputers. Meanwhile, small and mid-sized businesses - the true engine of innovation in most economies - were locked out. They either faced prohibitive costs for private computers, endured long queues for access to national facilities, or, worst of all, simply gave up and relied on guesswork.

But, thanks to new research by QuasiScience, in partnership with Cranfield University and Masters student Premkumar Bet, this picture has now changed. We recently tested whether accurate, repeatable, and cost-effective simulations could run on scalable cloud systems, and opened the door to more inclusive, flexible, and sustainable innovation.

Why Computational Fluid Dynamics Matters

Among the many types of simulation, Computational Fluid Dynamics (CFD) plays an outsized role in shaping modern industries. Aerospace engineers, automotive designers, and even healthcare innovators rely on CFD to understand how airflow, temperature, turbulence, and aerodynamic forces will change as they change their designs. It's at the heart of designing quieter aircraft, more fuel-efficient vehicles, better ventilated buildings, and safer medical devices.

CFD is significantly cheaper and faster than traditional physical testing, but it's computationally demanding. On top of this, the most valuable insights often emerge when CFD is combined with thermal analysis, structural mechanics, and control systems; and heavily automated to avoid manual data-entry errors. That creates an enormous strain on traditional computing systems.

To address these demands, many organizations turn to high performance computing (HPC) clusters - essentially, networks of powerful machines that split a problem into parts and solve them in parallel. This approach can shrink a simulation that would take weeks on a single desktop down to hours.

But there are problems with clusters too:

Concentration of capacity: North America holds a disproportionate share of global HPC infrastructure. Access for companies elsewhere is limited.
Capital intensity: Setting up a private cluster often requires millions in upfront investment, as well as ongoing spending on specialised teams to maintain it.
Environmental footprint: HPC clusters consume massive amounts of energy, and generate significant carbon emissions.

The result? Advanced simulations remained the preserve of the few, leaving countless innovators underserved.

Our Approach

At QuasiScience, we wanted to change all this. We decided to test whether accurate, repeatable, and cost-effective simulations could be run on affordable, scalable cloud systems. We partnered with Cranfield University, a world leader in aerospace research, and Masters student Premkumar Bet, to explore this challenge.

We decided to design and deploy a scalable system to support parallel CFD simulations, that ran not on a supercomputer, but on the cloud (specifically, the widely-used Amazon Web Services). We also set up software to automatically assign different part of the jobs to different cloud computers so they work together smoothly; built a step by step system to run the entire simulation automatically, from setting up the model to running the calculation to collecting the results; and tested our results against existing systems.

For benchmarking purposes, we ran several classic CFD cases that have been widely studied in the literature, and we compared our results to results from Cranfield University's Crescent HPC cluster.

The outcomes were as we expected in terms of accuracy, but exceeded our expectations in terms of cost. Without any particular optimisation, the costs we measured were three times lower than Cranfield's system for running the same exact simulation. And, because the cloud system can scale up or down seamlessly, we could do as many jobs in parallel as we wanted, lowering turnaround times for all the cluster users.

Clear Competitive Advantage

The implications go far beyond engineering departments. Affordable, cloud-based simulations create strategic advantages across industries. This development means:

Democratization of innovation: Start-ups and SMEs can now run simulations that were once reserved for multinationals or well-funded research institutions.
Agility and speed: Cloud clusters can scale up or down instantly. Companies can accelerate R&D timelines, respond faster to market shifts, and seize first-mover advantage in competitive sectors.
Sustainability gains: As hyperscale cloud providers move toward renewable energy, by using cloud-based clusters, businesses can align simulation practices with ESG goals and regulatory requirements.
Cost efficiency: Using cloud-based computing, businesses will pay only for the computing power they need - whether it's an occasional spike in demand or continuous development cycles. Alternatively, hybrid models could combine in-house infrastructure with cloud capacity for optimal efficiency.
Geographic accessibility: Cloud-based clusters can be made available globally, bypassing regional shortages of supercomputing infrastructure.

Implications

The potential for cloud-based high performance clusters extends well beyond our initial aerospace example. Some of the industries likely to benefit most include:

Automotive: Simulating aerodynamic drag, battery cooling, and crash scenarios for electric vehicles.
Pharmaceuticals: Running molecular simulations to accelerate drug discovery, particularly for underfunded diseases where traditional supercomputing is unaffordable.
Energy: Optimising wind farm layouts or simulating heat transfer in next-generation nuclear systems.
Built environment: Designing buildings with improved airflow, natural ventilation, and reduced HVAC energy use.
Consumer products: Rapidly testing and optimising designs for sports equipment, wearables, or appliances, which are not high value enough to merit traditionally computational fluid dynamics simulations.

Urgency

Cloud-based simulations have arrived at a critical moment.

R&D costs are rising: companies face mounting pressure to innovate while controlling spend.
ESG imperatives are growing: Investors and regulators are demanding proof of lower carbon footprints.
Competition is global: Emerging-market players are seeking ways to compete with established Western and Asian incumbents.

Forward-looking executives should see cloud-based HPC not just as a technical upgrade, but as a truly strategic capability.

Implementation Strategy

There are three key steps to capture the benefits of high-performance clusters:

Identify pilots: Think about simulation/testing use cases that did not previously represent value for money.
Choose the right partners: Find technical partners who can not only run a cloud-based high performance cluster, but also integrate it effectively with your systems and advise you on how to use it strategically.
Institutionalise the shift: Train teams and revise governance to embed cloud-based simulations into your R&D cycle.

The organisations that move early will gain a twofold advantage: lower costs and faster insights today, plus the ability to scale innovation rapidly tomorrow.

Conclusions

Cloud computing has levelled the playing field. Simulations which once required multimillion-dollar infrastructure can now be done inexpensively, without compromising on accuracy, speed, or sustainability.

This isn't just a technical breakthrough. Cloud-based HPC will enable faster innovation in critical fields (like drug development for underfunded conditions), reduced costs for start-ups and SMEs with brilliant ideas but limited resources, and greener operations for every business that makes the transition.

Real innovation is no longer a private members club. With cloud-based HPC, it's accessible to anyone with vision and ambition. In short, cloud-powered simulations aren't just a technical evolution. They're a strategic enabler for the next wave of competitive advantage.

Smart Decisions in Uncertain Times

Tue, 21 Oct 2025 23:00:00 GMT

In today's fast-moving business environment, uncertainty is often the only certainty. Markets shift, customer preferences change overnight, and regulatory frameworks can change with every political cycle. For leaders making high-stakes decisions - whether allocating capital, setting pricing strategies, or entering new markets - traditional forecasting tools often fall short. Traditional statistical methods using static scenarios or deterministic models fail to capture the full range of possible outcomes, and may be foxed by extreme scenarios.

One solution to this problem is Monte Carlo Simulations. (We will cover others, such as Markov Chains and Uncertainty Quantification, in later articles).

Monte Carlo Simulations

Inspired by his uncle's gambling habit, mathematician Stanislaw Ulam hit upon the idea of using repeated random sampling to model uncertainty. Monte Carlo simulations are now widely used in finance, engineering, pharmaceuticals and research, but what are they? A simple example of a Monte Carlo simulation is a simulation to calculate the area of an geometrical shape for which you don't know the formula. Using the Monte Carlo method, you would inscribe the shape in a rectangle (for which it's easy to calculate the area), then scatter a random distribution of points over the rectangle. The proportion of points that land inside the shape give you a good idea of the proportion of the area of the rectangle that is taken up by the shape.

Infrastructure Portfolio Optimisation

Our client was an innovative energy company investing in a €400 million portfolio of renewable projects, which required them to manage multiple types of uncertainty including:

Intermittent production: Solar output fluctuates with cloud cover, time of day and seasonal changes. Wind power varies hourly and seasonally.
Shifting market conditions: Prices for electricity are influenced by demand spikes, fuel costs, and regulatory changes.
Portfolio effects: Interactions between assets—such as wind farms in different regions—can amplify or dampen risks.

Our client's team had decades of experience in the energy sector, but their existing tools lacked the flexibility to handle the complexity that comes with renewable energy.

Monte Carlo simulations were the solution. For example, when modelling energy production from wind, the rectangle from our previous example is analogous to possible wind speeds, and the random points are analogous to samples of wind speeds at different times of year/day. The area of the abstract shape is analogous to an estimate of expected wind speed over the year (which then gives expected power generation in uncertain conditions).

Bringing together several Monte Carlo, we helped our client understand:

Production variability for several classes of renewable assets.
Volatility of market prices.
Aggregated results across portfolios, reflecting how diversification can mitigate or magnify risks.

Then integrated these into a system which provided automated bid price optimisation, so that our client could confidently make offers balancing competitiveness and risk.

Why is it important?

Monte Carlo simulations are valuable in any situation where you need to translate uncertainty into actionable insight. They allow leaders to:

Optimise decision-making: test strategies under different conditions and select options that balance risk and reward.
Quantify risk with confidence: Instead of vague risk labels, Monte Carlo simulations provide a data-driven probability of success or failure under different conditions.
Plan for extremes: By exploring tail scenarios — rare but impactful outcomes — Monte Carlo simulations allow businesses to build resilience and avoid catastrophic surprises.

The result is a more disciplined, evidence-based approach to strategic decision-making, which is particularly critical in environments where stakes are high and volatility is the norm.

Here are a few more examples:

Finance: Assess portfolio risk, stress-test capital plans, and price complex contracts.
Supply chain: Plan inventory under uncertain demand, evaluate supplier risk, and optimize logistics strategies.
Marketing and Sales: Forecast revenue under variable adoption rates, test pricing strategies, and optimize promotional spend.
Mergers and acquisitions: Quantify deal risk, simulate synergies, and stress-test assumptions.

Governance and Adoption

As ever, it's not all about the tech. To maximise the value of Monte Carlo simulations, business leaders should consider these questions:

What are your decision-critical uncertainties? Only you know which few variables drive the most risk or opportunity. Modeling every detail rarely adds value.
Is this tool usable? Insist that developers provide tools that integrate with existing workflows, and present insights in formats you can use.
How does this tool complement human judgement? Monte Carlo simulations provide probabilities, not guarantees. Leaders need to use outputs to inform strategy.
How should we iterate this tool? In rapidly-evolving industries, tools should be regularly updated to reflect new priorities. Ensure your tools have through-life support.
How do I build confidence in this tool? Even the best tool is useless if it is not trusted. Complement new tech with the right training and governance.

Tools for Risky Times

Uncertainty is inevitable, but chaos is optional. Tools such as Monte Carlo simulations enable leaders to see not just what might happen, but how likely it is, what drives it, and how to respond strategically in an unpredictable world.

But it's not all about the tech. The best engineering teams will ensure simulations are integrated with your existing systems, scale with your company, and are widely adopted, thanks to effective training and governance.

Get in touch today so we can help you model your toughest decisions and turn uncertainty into an opportunity.

Digital Twins for FIP MEC S.r.l.

Mon, 20 Sep 2021 00:00:00 GMT

[London, UK] - [September 9, 2021] - QuasiScience, a growing Simulation and Data Science company, will working alongside FIP MEC Srl to develop digital twin solutions for the construction industry.

Components for large-scale infrastructure must be tested comprehensively to ensure they meet structural safety, durability, and performance standards. The full testing process typically includes design validation, material testing, component-level testing, full-scale functional testing, environmental testing, durability and fatigue testing, certification and compliance testing, and rigorous field testing.

Testing is a major expense for infrastructure companies, but historic data which could accelerate this process is often stored in a way which does not enable engineers to exploit it.

Working with FIP MEC Srl, a leading Italian mechanical engineering company, Quasiscience developed a new digital twin software to organise, track and exploit pre-existing lab results, enabling simulation of the performance of new components.

The software can be used to develop parts for major projects from bridges to skyscrapers.

About QuasiScience

About FIP MEC Srl

FIP MEC Srl is a leading company in the production of structural bridge bearings, expansion joints, anti-seismic devices, fittings for tunnels and accessories for civil engineering and infrastructure.

Contacts

PR Team

Faster Design and Prototyping

Mon, 26 Sep 2022 23:00:00 GMT

FIP MEC Srl is an Italian company with a long tradition in developing system that are safety critical. These components are expensive to manufacture and always need to pass stringent tests that measure the. Each time a prototype fails at testing the company needs to bear the cost of material, labour, and disposal. We engaged with them in an investigation to demonstrate how a digital twin built on previous data would be able to drastically reduce development time and costs.

Challenges

FIP MEC Srl had a state of the art laboratory but lacked the infrastructure needed to clean up and organise the unstructured results produced during testing and the formal certification process. This caused a bottleneck in the development process, as engineers had to spend a lot of time manually analysing data and running simulations to understand the results of tests and to design new prototypes. Furthermore, in cases when the test data was lost or corrupted, the company had no way to recover it, leading to costly delays and the need to repeat tests.

Solution Design

Given the challenges faced by FIP MEC Srl, we designed a solution that would allow them to clean up and organise their data, and to build a digital twin that could be used to simulate the behaviour of their components under different conditions. The digital twin was built using machine learning algorithms that were trained on the historical data produced during testing. This allowed us to create a model that could predict the behaviour of the components under different conditions, and to identify potential issues at design time, before the components were manufactured and tested.

Implementation

Data Collection

The first step in the implementation process was to collect and clean up the data produced during testing. We worked closely with the engineers at FIP MEC Srl to understand the data and to identify the relevant features that could be used to train the machine learning algorithms.

The raw data was composed of a series of reports coming from different testing machines in the internal lab and from the external certification process. We developed a pipeline that was able to extract the relevant information from these reports and to organise it in a structured format that could be used for keeping the Engineering team informed and for training machine learning models.

Digital Twin

Once the data was collected and organised, we trained a machine learning model to create a digital twin of the components. Given that this model was going to be used in a safety critical context, we focused on building a model that was not only accurate but also interpretable, so that engineers could understand the predictions and the underlying reasons behind them. We built the model by combining different sub-models that were able to capture different aspects of the behaviour of the components, such as their mechanical properties, their response to different loads, and their failure modes. This allowed us to create a digital twin that was able to simulate the behaviour of the components under different conditions without losing interpretability.

Deployment

FIP MEC had an IT Team and on premise cluster to run most of the software for internal use. Hence, we designed the system in such a way that could be delivered both on prem and cloud.

Summary

This was a landmark project for QuasiScience: it represented our first engagement outside the UK and the first consulting project. As a byproduct of this work, our team developed core capabilities to take a product from inception to market in a very short time frame and with a clear focus on the client.

The software originally developed to answer FIP MEC's business needs has seen many updates and iterations over time. We called it is now available to all businesses that look for a tailored and effective experiment tracking software and results and accelerating the development process.

Beyond Chatbots

Wed, 06 Aug 2025 23:00:00 GMT

AI customer service is a phrase that evokes a shudder. We have all experienced unsatisfying conversations with AI assistants, which often harm, not help customer experience. But in today's world, there is far more to customer experience than the helpline. Ideally, service businesses should avoid calls or messages to their helpline - to do this, they must create a positive, efficient and user-friendly experience on all the platforms that customers use to access their services.

In May 2024, QuasiScience began working with Coyzy, an Italian company promoting psychological safety and well-being through a digital platform matching users with psychologists. For a company focused on mental health, it is doubly important to provide a user experience that is simple and appealing, offering customers a feeling of immediate safety and security, rather than adding to their frustration.

QuasiScience was able to help Coyzy improve their user experience by:

Making their the customer experience uniform across different devices, integrating seamlessly with established platforms, to enable easy access to services. This required QuasiScience to manage user identities across web and mobile platforms that were built at different points in time with different technologies
Improving the efficiency with which customers could book, track and reconcile appointments
Tracking service usage accurately to ensure users received the service they had subscribed to according to the tiered subscription model, integrating data from third-party services with internal data

Contact QuasiScience now to discuss your customer experience challenges.

Sound Mathematics Cloud

Sun, 31 Mar 2024 00:00:00 GMT

Sound Mathematics is an innovative UK startup that specialises in non-destructive testing. Their research team has won numerous grants and awards for their core technology: a Machine Learning algorithm producing full health reports for metallic components. When we met their team the first time, they were at the late stage of building their first MVP (or minimum viable product). However, they were not set up to deliver their insights as a service. They needed robust and cost efficient software architecture to start serving their first clients. We decided to partner with them to build and host all their inference pipelines on our cloud.

Challenges

After compiling a full list of requirements for the architecture, we highlighted the key ones to guide our decision making:

Users of the service are required to upload large files.
We return a complete analysis in a matter of seconds.
Preferably the cost at rest should be as close as possible to zero without impacting the potential to scale.
Project duration: less than a month to avoid impacting commercial partners.

Implementation

Serverless

We designed and built a serverless architecture to store, process, and return predictions on a large number of files. The architecture is based on AWS Lambda, S3 and DynamoDB. This allows us to scale horizontally and only pay for the resources we use.

Containerisation

We containerised the inference code using custom built OS images to reduce the memory footprint by ensuring that only the necessary libraries and dependencies are included. This allows us to easily update the code and scale the service without worrying about dependencies or compatibility issues.

Multistage Processing

Users are expected to upload large files to our backend together with special configurations. This process can be slow for the user and costly for us. We split the inference process atomically to catch user errors as quickly as possible and allow users to break up the data upload and result collection process.

Results

Thanks to the joint effort of our teams, Sound Mathematics was able to quickly integrate the APIs we built for them in their frontend and cloud architecture to engage with their customers and keep moving forward with commercialisation.

Saving coastlines with simulations

Mon, 20 May 2024 00:00:00 GMT

Across the world, coastal erosion threatens infrastructure, ecosystems, and cultural heritage sites. To help address this problem, QuasiScience built an intelligent system capable of detecting, analysing, and predicting erosion patterns by combining satellite imagery with historical weather and ocean data to highlight high-risk areas, identify where erosion was likely to progress fastest, and prioritise locations requiring intervention.

Coastal zones provide ecological and socioeconomic services but sea-level-rise will worsen coastal erosion and the cost of inaction is substantial.

— World Bank, 2023

Cost of Erosion

The costs of coastal erosion are vast - not just financially, but also environmentally and culturally. Annual losses to housing, transport infrastructure, biodiversity, and heritage sites are escalating across Europe. Yet most monitoring remains reactive: decision-makers frequently intervene only after erosion is visibly advanced or damage has already occurred.

As part of QuasiScience's programme of environmentally-friendly research, we decided to build an early-warning intelligence platform capable of predicting erosion risk before critical thresholds are reached.

Next-Gen Solutions

We built a technical prototype focused on modelling shoreline change drivers with precision and scalability. It featured:

Satellite Imagery Analysis: Multi-temporal shoreline extraction to detect long-term regression patterns and sediment movement.
Historical Weather & Climate Integration: Including data on wind, wave energy, storm frequency, tidal cycles, precipitation and seasonal variability.
CFD-Based Coastal Dynamics Simulation: Modelling how water movement, storm surges and local topography accelerate or slow erosion.
Predictive Erosion Risk Modelling: Combining imagery, climate data and hydrodynamic simulations to forecast where problems are most likely to emerge.

The framework can support governments, insurers and ecological agencies with a forward-looking tool to predict high-risk erosion zones.

As climate-related risk rises, tools like this that merge satellite intelligence, climate history and physics-based modelling will become essential for national resilience planning.

Revolutionising the Engineering Design Cycle

Sat, 15 Jun 2024 00:00:00 GMT

Modern cars, planes and other high manufacturing objects require a complex, highly collaborative design process from engineers all over the world. Current CAD tools have struggled to adapt to this because of the traditional point & click interface, a large, complex CAD Kernel and reliance on powerful workstations.

Working with Kioko, a groundbreaking Franco-British startup, QuasiScience has revolutionised this process by developing an integrated CAD and testing system for engineers. The new platform, alleviates many of the current limitations of CAD to answer the needs of today's engineers. The platform is the equivalent of Github and Visual Studio for 3D designs, enabling not only object visualisation, but also comprehensive testing.

This moves CAD into the 21st century

-- London Tech Week, 2024

Evolving Industry Needs

All engineers are familiar with the traditional design cycle: defining user requirements, developing ideas, planning and creating a solution, and rigorous testing. But this step-by-step description is no longer effective. The highly collaborative, international, and complex process of modern design requires rapid, iterative feedback, and increasingly uses digital simulation rather than physical testing.

Our client, Kioko, was developing a next generation CAD platform, and wanted it to reflect these changes. They decided to enhance their tool by enabling users to evaluate the performance of components at the same time as designing them, using the latest CFD mathematics.

Pushing the Boundaries

QuasiScience developed a high-performance prototype, featuring:

High-speed infrastructure: Built entirely in Rust, providing fast, memory-safe execution for simulation pipelines.
Smooth integration: Connecting design points exported from the CAD tool directly to CFD software to evaluate component performance.
Immediate feedback: using machine learning models to accelerate feedback, trading minimal accuracy when total precision was not required.

Next Steps

Kioko and QuasiScience are continuing their partnership to continue developing and refining this innovative platform to meet the needs of engineers across industries. The goal is to make it the go-to tool for engineers, enabling them to design and test in a single, seamless environment. This will not only speed up the design process but also lead to better, more innovative products in the market.

Land Selection for Renewables

Mon, 26 Sep 2022 23:00:00 GMT

Investors and landowners require precise, site-level assessments to identify viable land parcels for solar power. These must draw on a wide range of data sources, and require significant expertise to produce.

For our client, we created an automated, web-based land-suitability platform that merged government perimeter data, ownership records, and environmental indicators using advanced geospatial analytics, to streamline selection of optimal solar-farm locations. This transformed a slow, manual process that took days per site into an automated pipeline that took only minutes for each decision.

Bottlenecks in Project Finance

As Europe accelerates its renewable energy commitments, the demand for utility-scale solar has surged. Yet identifying suitable land parcels remains one of the industry's most operationally complex bottlenecks. Public records are fragmented, cadastral boundaries vary in quality, environmental constraints are scattered across multiple data providers, and manual assessments can take days or even weeks for a single site. The result is a slow, costly, and error-prone process that limits developers' ability to scale portfolios or compete in land negotiations.

Our client aimed to enter the solar development market with a data-driven edge. Their challenge was clear: evaluate thousands of allotments to determine which were technically viable, commercially attractive, and administratively feasible. This required integrating heterogeneous geospatial datasets, validating boundaries, calculating usable area, and assessing suitability against multiple constraints such as land shape, proximity to grid infrastructure, and ownership status.

The stakes were material. A single unsuitable parcel can cost millions in sunk feasibility work, while a timely, accurate evaluation can unlock early stage rights and investor interest. Our client needed a scalable system that translated raw geospatial data into clear development decisions.

Scaling Up

QuasiScience built a web-based geospatial decision platform that automated land analysis to identify high-potential solar farm locations. The approach combined rigorous data engineering with sound business logic to transform messy public records into actionable insights, including

Automated land suitability scoring: government allotment perimeters, ownership records, and environmental attributes integrated into a unified spatial model, enabling rapid filtering of viable parcels.
Flexible data exploration: an interactive map interface where users could visually inspect parcels, compare attributes, and export candidate sites for further feasibility work.
GIS-native processing: quick and accurate processing of geographical data at scale.

Land Use Planning

Massive speed improvement: We automated a process that previously required days per parcel to take minutes, enabling rapid, scalable portfolio-wide screening.
A single source of truth: By unifying perimeter data, ownership records, and suitability factors, our system replaced multiple disjointed workflows.
Investment-grade transparency: The platform provided defensible, traceable logic for land selection, improving confidence for investors, regulators, and internal stakeholders, and enabling our client to secure investment in their 1.2GW portfolio of solar farms.
Scalable foundations for future expansion: The data architecture and GIS engine were built to accommodate national-scale land datasets, positioning our client for aggressive market growth.

Are you working in a land-intensive industry, and struggling to manage uncertainty in your investments? Contact us today to unlock faster, more data-driven decision-making.

Project Finance Simulations

Sat, 15 Mar 2025 00:00:00 GMT

Renewable assets are increasingly central to forward-thinking infrastructure investors. But pricing is more of a challenge for unreliable renewables than for traditional energy sources. Variability in weather patterns, regulatory changes, and market dynamics all contribute to the complexity of valuing these assets.

Our client, a new energy company investing in a portfolio of renewables projects, turned to QuasiScience for a bespoke simulation system to estimate returns on renewable assets. Our model helped them value accurately a diverse portfolio or renewable energy projects, in preparation for a major acquisition as they expanded their market presence across Europe.

You guys rock!

— Remy Marino, CFO, Ortus Climate

Valuation Challenges

In today's rapidly changing energy market, renewable assets are increasingly central to forward-thinking energy investors’ and producers’ portfolios. But with this opportunity comes complexity. Renewable energy, whether it is wind, solar or hydroelectric, is subject to fluctuating production patterns, shifting market conditions, and (sometimes rapidly) shifting policy frameworks.

The business pages are littered with examples of failed, high-profile renewables projects: from BP's $1.1bn write-down of offshore wind projects to the failure of Saudi Arabia's $200bn solar facility.

Our client was investing in a new portfolio of renewables installations, and wanted a reliable way to predict their financial performance and set prices. They had decades of experience in the energy sector, but their existing tools lacked the flexibility to handle the complexity that comes with renewable energy.

Breaking Down Complexity

Our client needed a model that would take account of:

Intermittent production: Solar output fluctuates with cloud cover, time of day and seasonal changes. Wind power varies hourly and seasonally.
Shifting market conditions: Prices for electricity are influenced by demand spikes, fuel costs, and regulatory changes.
Portfolio effects: Interactions between assets—such as wind farms in different regions—can amplify or dampen risks.

Using advanced Monte Carlo simulations, we build a reliable evidence-based system to predict returns on different installations, taking account of these factors. Then, we added:

A custom workflow that translated outputs into the client's existing systems in an intuitive, user-friendly way
An automatic bid-price optimisation tool in which staff had only to set the level of desired risk (e.g. 95% probability of returns exceeding a threshold) to receive the optimum bid price for competitive power-provision tenders, balancing risk and competitiveness.

As ever, it's not just about the tech - integrations, training, governance and security are the key to turn smart maths into useful tools for businesses.

Tech and Decision-Making

Using our system, the Ortus team:

Were able to support their expansion into new markets across Europe
Were able to make business decisions based on evidence and share a common language across teams
Began construction of a pipeline of 1.2 GW of solar projects and 1 GW of wind projects

In today's fast-moving business environment, uncertainty is often the only certainty. Traditional statistical methods fail to capture the full range of possible outcomes, and can't cope with extreme scenarios. But advanced simulations like the ones we used in this project enable business leaders to quantify risk with confidence, optimise decision-making, and plan effectively for extreme scenarios.

Well-implemented numerical simulations turn guesswork into rational decision-making, and are the key to navigating uncertainty with confidence.

Modernising Colorado's energy grid

Mon, 26 Sep 2022 23:00:00 GMT

As Colorado accelerates its transition to 100% renewable energy, it faces one major challenge - reliability. On days when the wind doesn't blow or the sun doesn't shine, the state still needs to maintain a stable supply and respond to fluctuating demands for energy.

Our client, a renewable energy company, wanted Colorado to invest in their new energy storage facility, but the state was put off by the significant upfront cost.

QuasiScience modelled demand for energy storage throughout Colorado's green transition and proved that flexible and reliable storage solutions will be operationally essential. Our client was then able to proceed to build a solid business case to secure financing.

The system will last, if properly maintained, a century or longer. The capital investment up front is quite high, but when you run the financial models over 30, 50 or 60 years, this technology is, hands down, the cheapest technology on the market for storage.

— Peter Gish, Principal, Ortus Climate

The high cost of going green

Despite their environmental benefits, power sources such as wind and solar are by their nature intermittent, which introduces major challenges for national grids, who need to maintain a stable supply and respond to fluctuating demands for energy.

This challenge makes energy storage and emergency power control crucial capabilities. Our client, an innovative renewable energy company, wanted to secure investment for a hydropower reservoir, which uses excess energy to store water at height so that its potential energy can be converted into electricity when required.

But these facilities are expensive to build, with upfront costs of >£1bn. Our client needed to prove why this capability would offer a good return on investment.

Numerical simulation de-risks decision-making

Using advanced numerical simulation and optimisation techniques, QuasiScience modelled how demand for energy storage changes depending on the uptake of renewable energy. We:

Tested different grid and market scenarios to quantify risk and opportunity.
Optimised operational and investment strategies for different scenarios.
Provided actionable insights for decision-makers, proving the critical role of energy storage as the proportion of renewable energy in the grid increases.

Evidence-based insights unlock high-value deals

Thanks to QuasiScience's analysis, our client was able to secure:

Investment readiness: The analysis provided the evidence needed to convince investors of the viability of the project.
Competitive advantage: The insights gained from the simulations allowed our client to differentiate themselves in a competitive market.
Long-term planning: The ability to model different scenarios enabled our client to plan for the future with greater confidence.

Sustainable Goals

QuasiScience's study reframed Colorado's conversation about hydropower energy storage: this capability is not just a contribution to aspirational sustainability goals, it's strategically essential. Thanks to QuasiScience's analysis, our client was able to demonstrate the strategic value of hydropower storage, linking the capability to measurable reliability gains; as well as to Colorado's clean energy targets.

Numerical simulations and optimisation can de-risk businesses and make investors' decisions easier, and give leaders the confidence they need to make the investments that really matter.

Hydropower storage has now been established as a cornerstone of Colorado's renewable energy future.

On-Demand Rendering for Studios

Fri, 06 Jun 2025 00:00:00 GMT

As visual effects and animation production increasingly rely on high-fidelity rendering, long processing times can cause significant delays. Working with Nulight studios, QuasiScience developed a cloud-based system to accelerate rendering tasks, which gives smaller studios access to high-performance rendering without significant infrastructure investments.

Hidden Costs of Visual Effects

Rendering high-quality visual effects for film and animation requires a lot of computing power. For studios without extensive hardware, this creates bottlenecks that delay production.

Our client, Nulight studios, was developing a VFX plugin, which meant they needed to render large sequences to test and compare various options for the tool efficiently. But, using traditional rendering technology, this was prohibitively slow and costly.

Alternative Approach

QuasiScience decided to accelerate the testing process by moving rendering to the cloud. This solution meant:

Accessibility: Pay-per-use cloud-based solutions can enable high-performance rendering capabilities without significant upfront investment in infrastructure.
Scalability: Cloud-based resources are used only when they are needed, and can be scaled up and down rapidly.
Computing power: Just like a traditional, hardware-based HPC, cloud-based rendering can perform tasks in parallel, enabling high-throughput processing.
Rapid model validation: Nulight Studios was able to complete their first AI-based VFX plugin in only 3 months - a process that could otherwise have taken months more.

This system provided the client with not only affordable power, but also speed and flexibility, making it possible for a relatively small studio to produce a truly innovative VFX product.

Open Access Beta

We are currently working on a ready-to-use beta model of our cloud rendering infrastructure to provide studios of all sizes with scalable, high-throughput rendering capabilities. Reach out to our team via our contact page to learn more about how we can help accelerate your visual effects production.

AI-Powered Film Editing

Sat, 31 Jan 2026 00:00:00 GMT

Machine learning has opened up extraordinary opportunities for the visual effects industry. Labour-intensive tasks like object removal and scene cleanup can now be automated. Working with Nulight studios, QuasiScience developed an award-winning tool for object removal and imperfection correction, which won a £50k award for Amplifying Imagination: AI in the Creative Industries, sponsored by the BBC, AWS, Innovate UK and the DIgital Catapult.

A game-changer for us at Nulight Studios

— CEO, Nulight Studios

Common Challenges in Film Editing

Video-editing is a creative profession, but traditional post-production work requires extensive manual frame-by-frame editing to remove unwanted objects or imperfections. This time-consuming process limits editors’ availability for more creative tasks.

Our client, Nulight Studios, wanted to use the latest machine learning technologies to automate these repetitive tasks. They called in QuasiScience to help.

AI At Work

Our team of data scientists developed a custom VFX plugin which enabled:

AI-powered object removal: automatic detection and removal of unwanted objects and imperfections in video frames.
10x quicker editing: no need to fill empty spots after object removal
Seamless integration: a plug for existing software pipelines, which didn't disrupt other work.
Improved visual quality: consistent, high-quality results across diverse types of footage.
Scalability: a repeatable, automated process that can be applied to multiple projects.

Our plugin combines high-performance processing with intuitive controls, and frees up creatives to focus on creativity.

Based on this work, Nulight Studios won a £50,000 award for Amplifying Imagination: AI in the Creative Industries, sponsored by the BBC, AWS, Innovate UK and the DIgital Catapult.

Future of Film Editing

This tool is a milestone not only in the video editing industry, but also in the tense conversation about creativity and AI. At QuasiScience, we believe that AI does not exist to replace creative jobs or lessen them, but to free them up from dull, repetitive tasks and enable them to focus on the more inspiring, imaginative work that they actually want to do.

Do you have a dream VFX plugin you'd like to develop? If so, reach out to us at QuasiScience - we'd love to help you bring it to life.

Recommendation Engines in Retail

Sat, 31 Jan 2026 00:00:00 GMT

As retailers face increasing competition, online recommendation quality has become a major driver of conversion and customer lifetime value. Our client, a S&P 500 listed fashion retailer, asked QuasiScience to improve their systems. We developed a suite of advanced, context-rich recommendation models, generating 3% uplift in total sales, worth several million dollars.

Consumers are expecting personalised experiences; they expect that [we] know who they are — not just that we recognise them when they are online, but wherever they are interacting with the brand.

— Mary Beth Laughton, EVP of U.S. Omnichannel Retail at Sephora

As retailers compete in an environment where customer expectations for personalisation are higher than ever, even small improvements in relevance can translate into large revenue gains. Digital channels are a key arena in which to influence purchase decisions, so recommendation systems that surface the most compelling products, improve conversion rates, and maximise cart value, are crucial to success.

Our client, a major retail brand, was using simple recommendation rules (such as 'most bought items') that failed to incorporate data on user behavior. This meant they could only offer static, low-context suggestions that neither reflected individual preferences nor adapted to the shopper's intent. Our client knew that millions in potential revenue were being wasted, but lacked the modeling infrastructure to capture this opportunity.

True Personalisation

QuasiScience designed a comprehensive, multi-layer recommendation framework that captured far richer context around each customer and their product interactions. Key components included:

Context-Rich Models: Drawing on transaction data, location data, and records of users activities to understand both long-term preferences and immediate intent.
User and product-based recommendations: Tailored 'you might also like' suggestions, as well as complementary and similar item recommendations.
Dynamic experience optimisation: so teams could test and deploy different recommendation strategies and measure their impact in real time.

We implemented these models using scalable pipelines that fit seamlessly into the client's existing digital infrastructure, minimising the amount of training and adaptation required for staff.

Wins for both customers and the business

The project delivered significant commercial value:

A 3% uplift in sales across various categories, totalling an additional £2m over the first year of the pilot.
Faster, evidence-based decision-making, as merchandising, digital, and marketing teams now have clear evidence on which types of recommendations perform best.

Applications Across Industries

Recommendations are not only relevant to the world of fashion - they are an essential tool for any large-scale B2C business. And they also extend beyond the online retail environment - similar techniques can be used to offer real-time in-store personalisation, audience-level targeting, and next-generation omnichannel experiences. However you use them, one thing is clear: cutting-edge recommendation engines are crucial for competitive advantage in an increasingly data-driven retail landscape.

Price Adjustments in Retail

Sat, 31 Jan 2026 00:00:00 GMT

As the global fashion market becomes subject to ever-faster shifting customer demand, the pressure is on for retailers to make smarter, faster pricing decisions. Our client, an S&P 500 fashion brand, was struggling to keep prices aligned with changing market conditions.

QuasiScience created a practical pricing model that helped them adjust prices confidently, incorporating currency fluctuations, stock levels, and expected demand. This increased revenue by 4%.

FX and regional demand are exerting a significant pressure on margins

— Jean‑Jacques Guiony, CFO of LVMH, an industry leader

Fashion Fades

The fashion industry, and in particular fashion's eCommerce landscape, is becoming harder to navigate. Prices are updated rapidly and global currency movements affect the real value of every transaction. For retailers selling across borders, small shifts in exchange rates or demand can have a massive impact on profits.

Our client faced this challenge daily: prices set one day no longer made sense the next, and differing stock levels across regions meant some products sold out too quickly while others were overproduced. Without a clear way to stay ahead of these shifts, the team was leaving value on the table.

Evergreen Solution

Using advanced simulation and optimisation mathematics, QuasiScience developed an easy-to-use pricing model that enabled our client to adjust prices as conditions changed. Our model provides:

Automatic price optimisation: Practical recommendations that balanced attractiveness to customers with revenue goals and stock availability.
Scenario-based options: Simple scenario-based views so the team could model how different pricing choices might affect revenue as the context changed.
Evidence-based decision making: Easy-to-use alerts indicated when and how prices should be updated.

The tool took the guesswork out of pricing optimisation, and enabled our client to navigate an uncertain market with confidence.

Know Your Assets

The tooling developed by QuasiScience transformed the team's work. It led to:

4% revenue increases during peak seasons and high-volatility periods.
More consistent pricing decisions, reducing the guesswork that previously led to lost margin or missed sales.
Better alignment between teams helping teams maximise revenue across the organisation.
A stronger long-term pricing foundation, opening the door to broader revenue-management improvements.

And this tool is not only relevant in retail - it has broad applications across every B2C sector. QuasiScience is now developing similar tools for clients across industries.

Sales Briefing Automation

Sat, 31 Jan 2026 00:00:00 GMT

In fast-paced B2B environments, sales success depends on having a detailed understanding of customers. But many sales teams spend more time searching for information than speaking to their clients! Even with the best CRM in the world, it is time consuming for teams to refresh their memories of previous conversations and get the information they need to make a sale.

Our client, a global industrial manufacturer, asked us to solve this problem. We developed a chat-based agent that allowed sales teams to access relevant context instantly, leading to a 65% reduction in prep time, with no tedious manual data entry into a CRM tool.

By saving so much time on data-entry, this tool meant we could focus on the most crucial part of CRM - understanding human motivations and developing strategies.

— Sales Director, Global Industrial Manufacturer

Data Trap

Sales organizations are under increasing pressure to deliver personalized, data-backed campaigns, but most existing CRM systems are not up to the task. In reality, reps avoid using these tools due to the time required and the complexity of using the system, leading to data gaps, and missed opportunities.

Our client asked us for a solution that would allow sales reps to work in an intuitive way, using simple easy-to-use chat environments, but still gain the depth and precision that comes from the most detailed CRMs.

Conversational CRM

QuasiScience built a conversational agent that integrates directly with the company's IT architecture. It offers:

Conversational interface: Sales reps simply message the tool to receive insights, summaries, or data drawn from relevant project documents.
Automated context retrieval: Our tool identifies the client or topic being discussed and automatically finds relevant documentation.
Automatic data recording: Data is automatically captured and organized, ensuring accuracy without manual input.

Impact

Our tool had an immediate on efficiency and sales performance:

65% reduction in prep time
Access to consistent, complete data, captured automatically from conversations
Sales teams focused on strategy, not data-entry and retrieval
A 5% increase in sales in the first six months

Our tool turned sales preparation from a chore into a strategic capability, so sales teams could focus on relationships, not record-keeping.

Conversational intelligence is not just for sales teams. The same framework can be used for automatic retrieval of testing documentation; maintenance records; or order histories; meaning there are applications in every business area from R&D to customer service. Does your team have a data retrieval problem? Get in touch to find out how we can help.

Streamlined Materials Performance Testing

Sat, 31 Jan 2026 00:00:00 GMT

QuasiScience helped a leading advanced materials company transform how it manages and uses its testing data. Drawing on our experience building data systems for Formula One teams, we developed a unified materials data platform that centralised experimental results, automated data capture, and enabled more effective analysis and collaboration. This accelerated R&D, improved material selection, and preserved valuable institutional knowledge.

Effective data management can be the key to improving efficiency in materials research and opening up its added value.

— Dr Ben Thomas, Department of Materials Science & Engineering, University of Sheffield

Materials Testing

Industries from aerospace to automotive run hundreds of materials testing experiments every year. For any business involved in producing physical products, it's crucial to understand how different materials, or combinations of materials, surface treatments and adhesives perform under varying conditions.

However, many organisations are not making the most of this data. Results may be stored in disjointed spreadsheets, lab notebooks, or historic databases. This fragmented approach is far from the best foundation for research and decision-making.

Our client, a leading advanced materials company, wanted to unlock the power of its data by improving the way it collected, organised and analysed the results of experiments, past and present.

From F1 to the Factory

Drawing on our team's experience building high-performance data systems for Formula One teams, QuasiScience designed and implemented a unified data platform tailored to the client's needs.

When we worked in F1, we needed to track variables such as composite layering, surface coatings, adhesive types, and mechanical test outcomes, to select the optimal combination for each component, balancing strength, weight, and durability. We built a similar system, featuring:

Centralised Data Collection: Automated data capture from multiple lab instruments and testing set-ups.
Smart Organisation and visualisation: to make experiments easily searchable and comparable.
Custom analytical tools: Built-in visualisation tools to analyse trends, correlations, and trade-offs using custom metrics.
Collaboration Features: Live, secure, role-based access for engineers, researchers, and management.

Information management in R&D

QuasiScience's new data platform transformed the client's R&D process. For the first time, engineers and scientists could view all test results in one place, compare outcomes across experiments, and quickly identify promising combinations. This meant:

Faster innovation: Engineers reduced the time spent searching for past data and setting up redundant experiments.
Improved decision-making: data-driven selection of materials based on all the evidence available.
Knowledge retention: Institutional knowledge from years of testing became easily accessible, reducing reliance on individual experts
Enhanced collaboration: Multiple teams across different sites could access consistent, validated data, accelerating joint research.

The Good Data Advantage

Data infrastructure may sound like a dry topic, but without good databases and working data pipelines there is no methodology that could yield a good outcome. What is the use of advanced simulations, machine learning models or digital twins if the underlying data is poor quality, incomplete or inaccessible?

Good infrastructure makes sure data is clean (free of structural issues like typos, duplicates and missing entries); accurate; legally usable; secure and accessible, and opens up the opportunity for companies to use all the latest data science and research techniques.