Everything you need to know about Fluidmapper.

We serve organizations that need reliable hydrodynamic data to make engineering, scale-up, optimization, or model-development decisions. Today, we see three primary customer segments. 1. Industrial End Users Companies operating mixing-intensive processes where fluid dynamics directly impact performance, yield, energy consumption, heat transfer, or product quality. Examples:

• Chemical manufacturers
• Biotech and fermentation companies
• Food and beverage processors
• Mining and mineral processing companies
• Water and wastewater treatment operators
• Nuclear and energy companies These customers use Fluidmapper to optimize equipment, troubleshoot problems, reduce scale-up risk, and improve process performance. 2. Engineering, CFD, and Consulting Firms Companies that develop process designs, perform simulations, or provide engineering services. Examples:
• CFD consultancies
• Process engineering firms
• Reactor design firms
• Equipment OEMs These customers use Fluidmapper to validate simulations, improve models, provide higher-value services, performance certification, and increase confidence in engineering recommendations. 3. AI and Digital Twin Companies Organizations developing next-generation engineering software, AI models, and digital twins. Examples:
• Physics-informed AI companies
• Industrial AI startups
• Digital twin developers
• Simulation acceleration platforms These customers use Fluidmapper as a source of physics-grounded training and validation data. Secondary Markets
• Universities and research centers
• Government laboratories
• Technology developers
• Equipment manufacturers How we think about it strategically
• Short term: Industrial customers seeking answers to specific process questions.
• Medium term: CFD firms and engineering companies seeking validation and model improvement.
• Long term: AI companies seeking large volumes of high-quality experimental fluid dynamics data.

We have designed the process to be simple and low effort for the customer. Submit your request via the web site: Share your geometry, operating conditions, fluid properties, and the questions you want answered. Receive a quote: We review the requirements and provide a detailed quotation covering geometry preparation, calibration, tracer requirements, and experimental measurements. Approve and launch: Once the quote is approved, payment can be completed online by credit card to initiate the project. Geometry preparation and scheduling: We either use an existing geometry from our inventory, receive a customer-provided geometry, or manufacture a new one from CAD files. Once the geometry delivery timeline is confirmed, we provide an estimated delivery date for the datasets. Data delivery: Results are delivered as:

• Lagrangian time-series data
• VTU flow-field datasets
• Trained neural-network reconstruction model (optional)

Possible. Fluidmapper is currently offered exclusively as a service but we can discuss potential arrangements for licensing in specific applications and uses. Our platform combines specialized instrumentation, radioactive tracer preparation and activation, calibration, validation, AI reconstruction, and data processing. By operating the platform ourselves, we ensure consistent data quality, quantified uncertainty, regulatory compliance, and rapid project execution. This approach also removes the burden of permits, specialized personnel, training, calibration, maintenance, and validation from our customers. Our goal is simple: you focus on your process and engineering questions, and we deliver the hydrodynamic data and insights.

Not at this time. Fluidmapper currently operates as a centralized service from our own facilities. The technology relies on radioactive tracer measurements, which require specialized equipment, regulatory permits, radiation safety procedures, and trained personnel. Operating from a controlled environment allows us to ensure data quality, safety, compliance, and consistent project execution. Customers therefore send us their geometry or design information, and we perform the measurements in our facilities before delivering the resulting datasets. Looking ahead, we can envision deploying additional measurement units in strategic locations and potentially licensing the technology to qualified operators with the appropriate permits, infrastructure, and training. However, this is not part of our current business model.

It depends on the complexity and the size of the system, but for many industrial applications we can generate validated velocity fields in one day, which is significantly faster than a traditional CFD workflow. A typical CFD project often requires:

• Geometry preparation and cleanup
• Mesh generation
• Model selection and setup
• Computational runs
• Convergence verification
• Validation against experimental data
• Highly qualified personnel to operate and interpret the results For complex turbulent, multiphase, or large-scale systems, the process can take weeks or even months. With Fluidmapper, once the geometry is available and calibrated, we can typically generate a new dataset in approximately one day of measurement. A single calibration can then support multiple operating conditions without repeating the setup. The key difference is that we measure the physics directly rather than solving the governing equations numerically.

Not necessarily better in every situation, but fundamentally different. CFD is a predictive tool based on mathematical models and assumptions. Fluidmapper is a measurement platform based on observed physics. The key advantage of Fluidmapper is that the data is grounded in reality. We directly measure the flow and quantify the uncertainty associated with every reconstruction. CFD, on the other hand, relies on turbulence models, multiphase models, boundary conditions, and numerical assumptions that may or may not accurately represent the real system. For simple systems with well-understood physics, CFD can provide excellent results. For complex industrial systems involving turbulence, multiphase flow, fermentation, suspensions, or poorly characterized phenomena, the accuracy of CFD often becomes uncertain and requires experimental validation.

Many university laboratories have had access to Radioactive Particle Tracking technology for more than 40 years. Despite its technical value, the technology has remained largely confined to research because it was too slow, too complex, and too difficult to access for most industrial users. Academic groups can certainly perform isolated experiments, but they generally lack:

• Automated calibration
• Industrial throughput
• Large-scale validation infrastructure
• Standardized outputs
• Customer onboarding and project management systems
• Dedicated service delivery capabilities
• Commercial support and guaranteed turnaround times In addition, universities often operate under research-driven frameworks that can create friction for industrial customers. Projects may require lengthy contracting processes, intellectual property negotiations, multiple administrative approvals, and uncertain timelines before work can begin. Fluidmapper was specifically designed to remove these barriers. Our innovations include:
• AI-driven reconstruction and calibration
• Automated validation workflows
• Rapid geometry setup
• Standardized deliverables
• Quantified uncertainty
• Online quoting and project approval
• Fast project scheduling and predictable delivery timelines A customer can submit a request through our website, receive a quote, approve the project, pay online, and have the work scheduled quickly without months of contracting discussions. The measurement principle itself is not new. What is new is transforming a specialized academic technique into a practical industrial platform that companies can access as easily as any other engineering service.

AI enables reconstruction of high-resolution flow information from detector signals while dramatically reducing calibration effort. It also enables generation of large datasets that can be used to train future physics-informed models.

CFD-generated data inherits CFD assumptions. Real industrial systems often contain multiphase flows, non-Newtonian behavior, dead zones, and process-specific phenomena that are difficult to model accurately. We generate real-world data.

Yes. We believe one of the most strategic assets being created is a growing library of validated industrial flow fields. This dataset could become foundational for future AI models in process engineering.

Today, Fluidmapper operates as a measurement and data-generation platform. Once a geometry has been calibrated, multiple operating conditions can be tested rapidly, allowing us to generate large amounts of hydrodynamic data efficiently from a single setup. The business scales in several ways:

• Additional measurement platforms can be deployed and standardized.
• Existing geometries can be reused across multiple customers and projects.
• Calibrated systems can generate many datasets over their operational lifetime.
• Proprietary datasets continue to grow in value as more experiments are performed. Beyond project-based services, we see a significant opportunity to build strategic partnerships with equipment manufacturers, CFD companies, and AI developers. For equipment manufacturers, we can generate extensive datasets across entire product families, helping accelerate design optimization, improve performance, and reduce development costs. For CFD and AI companies, we can provide large volumes of physics-grounded hydrodynamic data to validate, calibrate, and train next-generation models. Our long-term vision is to become the hydrodynamic data infrastructure layer for the industry. Strategic Vision Just as cloud providers operate large-scale data centers, we aim to operate large-scale hydrodynamic data generation platforms. Our goal is to become the preferred source of validated fluid dynamics data for equipment manufacturers, simulation companies, and AI developers.

Selling equipment would reintroduce many of the challenges that have historically limited the adoption of radioactive particle tracking technologies. Customers would need to:

• Obtain and maintain regulatory permits
• Implement radiation safety programs
• Train specialized personnel
• Operate and maintain the equipment
• Perform calibrations and validations
• Manage radioactive tracer logistics
• Develop expertise in data reconstruction and interpretation Our service model keeps all of this complexity internal. Customers simply provide their geometry and operating conditions, and we deliver validated datasets and results. For most organizations, this approach is also significantly more cost-effective. Purchasing the equipment is only a small part of the total cost. The larger expenses come from permitting, staffing, training, maintenance, compliance, and maintaining sufficient utilization to justify the investment. By centralizing the infrastructure and expertise, we can spread these costs across many projects and customers, making high-quality hydrodynamic measurements accessible without requiring customers to become experts in the technology.

This was one of the most common concerns raised during customer interviews, and it stems from a misconception about the role of AI in our platform. Fluidmapper does not use AI to generate synthetic flow fields or invent trajectories. The data originates from real physical measurements. The role of AI is to reconstruct the position of the tracer from the measured radiation signals collected by our detector system. Once the tracer position is reconstructed, the resulting trajectory represents an experimentally measured path through the system. In other words:

• The radiation is measured.
• The tracer position is reconstructed.
• The trajectory is measured.
• The flow field is derived from measured data. AI is simply the reconstruction engine that converts detector signals into spatial coordinates, much like image reconstruction algorithms are used in medical imaging. To ensure trust, every reconstruction model is calibrated and validated against extensive experimental datasets generated using our robotic validation platform. We provide quantified uncertainty on the reconstructed positions and can report expected standard deviations in the x, y, and z coordinates. We therefore do not ask customers to trust a black box. We provide:
• Real measured data
• Independent validation
• Quantified uncertainty
• Transparent error metrics

Every project begins with a structured feasibility assessment. We evaluate the geometry, vessel size, process conditions, phases involved, measurement objectives, required accuracy, and expected engineering value. Rather than asking whether a measurement is simply possible, we determine the most practical and cost-effective strategy to generate the information needed to support your engineering decisions. This includes selecting the appropriate tracer dimensions, detector configuration, calibration approach, acquisition time, and expected deliverables.

Yes. Radioactive Particle Tracking has been successfully applied from laboratory reactors to industrial vessels several meters in diameter. As systems become larger, measurement design becomes increasingly important. Detector configuration, tracer activity, acquisition time, and calibration strategy are adapted to maintain the desired measurement quality. These are engineering considerations rather than fundamental limitations of the technology. Large, opaque, multiphase systems are often where Fluidmapper provides the greatest value because conventional optical measurement techniques become impractical.

No. Detector layout is designed for each application based on the vessel geometry, process conditions, required spatial resolution, and engineering objectives. Some applications can be measured with external detectors, while others may benefit from alternative detector arrangements. Our objective is to achieve the required measurement quality with the simplest and most cost-effective configuration.

Every tracer is engineered for the application. We select the tracer size, density, shape, and surface properties to ensure it follows the phase of interest as accurately as possible. The tracer is then activated using neutron irradiation to produce the required radioactive signal. Tracer design is one of the key engineering aspects of every project and is optimized to balance measurement fidelity, signal quality, acquisition time, and cost.

These situations are considered during project planning. If a tracer becomes trapped, the resulting data immediately indicates the loss of representative motion and the experiment can be stopped, allowing corrective action before additional measurements are performed. Similarly, if the tracer leaves the calibrated measurement volume, the reconstruction system identifies that the particle is operating outside its validated domain rather than silently generating unreliable results. Depending on the application, the measurement strategy can be adapted to include additional regions or operating scenarios.

Tracer robustness depends on the application. For every project, the tracer is designed to withstand the expected mechanical, chemical, and thermal environment. Applications involving centrifugal pumps, high-shear impellers, abrasive slurries, or elevated temperatures are evaluated during the feasibility assessment to ensure an appropriate tracer design.

The required measurement time depends on how quickly the tracer explores the entire flow field. Rather than selecting an arbitrary acquisition time, we estimate the duration based on vessel size, characteristic flow velocities, circulation patterns, and the desired statistical confidence. During the experiment, coverage and convergence metrics are monitored to determine when sufficient data has been collected. Our objective is to collect enough information to accurately characterize the hydrodynamics—not simply to run for a fixed period.

Radiation safety is integrated into every project as we operate under Canadian Nuclear Safety Commission (CNSC) permit pursuant to section 24 of the Nuclear Safety and Control Act . Tracer activity, detector configuration, shielding requirements, and operating procedures are determined during project planning in accordance with applicable regulations and the measurement objectives. Because Fluidmapper currently operates as a centralized service, customers do not need to obtain radioactive materials permits, develop radiation safety programs, or train specialized personnel. We manage all regulatory compliance and radiation safety requirements as part of the service.

Very little. Your team provides the CAD geometry, operating conditions, process information, and the engineering questions you want answered. Fluidmapper manages the entire workflow, including feasibility assessment, geometry preparation, tracer design, calibration, measurements, data processing, uncertainty quantification, and engineering deliverables. Your engineers remain focused on product development, process optimization, and customer support while we generate the experimental flow data needed to support those decisions.

Yes. One of Fluidmapper's unique capabilities is the ability to synchronize experimentally measured 3D velocity fields with other process measurements acquired during the experiment. Depending on the application, these may include dissolved oxygen, temperature, pressure, power consumption, torque, gas flow rate, liquid level, pH, gas holdup, bubble size distribution, or customer-specific sensors. By combining local hydrodynamics with global process measurements, engineers can better understand how flow structures influence overall process performance.

Velocity fields explain how fluids move, while process measurements explain how the process performs. Synchronizing these datasets allows engineers to directly correlate hydrodynamics with engineering metrics such as mixing time, oxygen transfer (kLa), heat transfer, gas dispersion, reaction performance, solids suspension, or energy consumption. Rather than analyzing individual measurements independently, Fluidmapper provides an integrated view of how flow behavior drives process performance.

Yes. Fluidmapper not only provides experimentally measured 3D velocity fields for CFD validation but can also synchronize those measurements with process variables such as dissolved oxygen, kLa, torque, power consumption, gas holdup, pressure, and temperature. This allows simulation developers and process engineers to validate both the predicted hydrodynamics and the resulting process performance, creating a much richer validation framework than velocity measurements alone.

Absolutely. Every project begins by identifying the engineering questions you are trying to answer. If additional process variables are important for your application, we can often integrate customer-specific sensors or data streams into the experimental campaign. Our objective is not simply to generate velocity fields, but to deliver the experimental data needed to answer your engineering questions.

Fluidmapper does not directly measure rheology like a laboratory rheometer. Instead, it provides experimentally measured three-dimensional flow fields that can be used to infer or calibrate the effective rheological behavior of complex industrial processes. This is particularly valuable for suspensions, gas-liquid systems, fermentation broths, crystallizing mixtures, and other multiphase processes where the apparent rheology evolves continuously as solids concentration, gas holdup, temperature, shear history, or particle interactions change during operation. By combining experimentally measured hydrodynamics with complementary process measurements such as torque, power consumption, gas holdup, pressure, temperature, and CFD or inverse modeling, engineers can identify constitutive models and rheological parameters that reproduce the observed flow behavior under actual operating conditions. Rather than characterizing a small laboratory sample, Fluidmapper helps characterize the effective process rheology inside the operating equipment, providing a more representative description of the material under real industrial conditions.

Yes. One of the emerging applications of Fluidmapper is the development and calibration of advanced constitutive models for complex industrial processes. Traditional rheology is typically measured on small laboratory samples under controlled conditions and assumes that material properties are homogeneous throughout the system. In many industrial processes, however, the apparent rheology evolves continuously as solids concentration, gas holdup, particle interactions, temperature, and shear conditions vary throughout the vessel. By combining experimentally measured three-dimensional velocity fields with synchronized process measurements, including torque, power consumption, pressure, temperature, gas holdup, solids concentration, and other customer-specific variables, Fluidmapper provides the experimental foundation needed to calibrate constitutive models under real operating conditions. This enables CFD developers to move beyond constant or globally averaged rheological properties toward spatially and temporally varying rheology models that more accurately represent industrial processes. Rather than relying exclusively on laboratory rheometer measurements, developers can use experimentally measured process hydrodynamics to identify the effective constitutive behavior experienced inside the operating equipment.

Most engineering surrogate models are trained using CFD-generated datasets. While these models dramatically reduce computation time, they also inherit the assumptions and limitations of the underlying simulations. Fluidmapper enables a new generation of surrogate models trained and validated using experimentally measured hydrodynamic data. By providing large volumes of physics-grounded 3D velocity fields with quantified uncertainty, developers can reduce the simulation-to-reality gap, improve model robustness, and increase confidence in industrial deployment. Beyond flow fields, Fluidmapper can synchronize experimental measurements with process variables such as torque, power consumption, dissolved oxygen, kLa, gas holdup, pressure, and temperature. This allows AI models to learn not only how fluids move, but how hydrodynamics influence overall process performance. The result is a new class of engineering AI capable of predicting both local flow behavior and global process outcomes, enabling faster design optimization, digital twins, process control, and inverse engineering.

The performance of a bioreactor depends on much more than impeller speed or power input. Oxygen transfer, gas dispersion, nutrient distribution, mixing time, shear, CO₂ stripping, and temperature uniformity are all governed by the underlying hydrodynamics. Fluidmapper provides experimentally measured three-dimensional flow fields under realistic operating conditions, allowing engineers to directly observe how design choices influence fluid behavior. By synchronizing these measurements with process variables such as dissolved oxygen, kLa, power consumption, gas flow rate, pH, temperature, gas holdup, and other customer-specific measurements, engineers can correlate local flow structures with overall bioprocess performance. Typical applications include:

• Designing and optimizing impellers, spargers, and gas distribution systems
• Meeting target kLa and oxygen transfer requirements
• Reducing shear while maintaining effective mixing
• Identifying dead zones, short-circuiting, and poorly mixed regions
• Comparing alternative reactor designs or operating conditions
• Supporting scale-up from laboratory to pilot and commercial production
• Validating CFD models and developing digital twins for bioprocesses
• Training AI surrogate models that predict both hydrodynamics and biological performance Rather than relying solely on empirical correlations or simulation assumptions, Fluidmapper provides objective experimental data that helps engineers design bioreactors with greater confidence while reducing development time and technical risk.

Yes. While Fluidmapper's primary output is experimentally measured three-dimensional hydrodynamic data, the platform can simultaneously acquire and synchronize complementary process measurements such as dissolved oxygen, kLa, pH, temperature, power consumption, gas flow rate, gas holdup, and other customer-specific process variables. This enables engineers to directly correlate local flow structures with key performance indicators, providing deeper insight into oxygen transfer, mixing efficiency, shear environment, heat transfer, and overall bioreactor performance. Instead of analyzing hydrodynamics and process performance independently, Fluidmapper provides an integrated dataset that links how the fluid moves with how the biological process performs.

Developing a new agitator or mixing system typically requires multiple design iterations involving prototype fabrication, CFD simulations, experimental testing, and customer validation. This iterative process is often slow, expensive, and technically demanding, particularly for opaque, multiphase, or industrial-scale systems where direct flow measurements are difficult. Fluidmapper dramatically shortens this development cycle by rapidly generating experimentally measured three-dimensional flow fields directly from your CAD geometry. Rather than relying solely on CFD predictions or custom visualization experiments, engineers can quickly compare multiple impeller designs, blade geometries, baffle configurations, spargers, or operating conditions using objective experimental data. Because geometry preparation and calibration are performed only once, multiple operating conditions can be evaluated rapidly, enabling cost-effective design optimization. Typical applications include:

• Comparing impeller designs before fabrication
• Optimizing blade geometry, pitch, diameter, and clearance
• Evaluating impeller spacing and multiple-impeller configurations
• Designing spargers and gas dispersion systems
• Optimizing pumping capacity, mixing time, and circulation patterns
• Identifying dead zones, short-circuiting, and poorly mixed regions
• Reducing power consumption while maintaining process performance
• Supporting customer performance guarantees with experimentally validated data By reducing the time and cost required to evaluate new concepts, Fluidmapper allows engineering teams to explore more design alternatives, accelerate product development, and bring better-performing equipment to market faster.

Traditional experimental development often requires custom transparent vessels, optical access, model fluids, and specialized instrumentation, making each project expensive and time-consuming. These test rigs are typically designed for a single geometry and rarely reproduce the actual operating conditions found in industrial equipment. Fluidmapper eliminates this burden by providing Flow Data as a Service. We manage the entire experimental workflow, from geometry preparation and tracer design to measurements, data processing, and engineering deliverables, allowing your engineers to focus on product development rather than building experimental infrastructure. The result is faster design iterations, lower development costs, increased engineering capacity, and experimentally validated performance data that supports both engineering decisions and customer confidence.

Equipment performance guarantees are only as strong as the evidence supporting them. Fluidmapper provides experimentally measured three-dimensional flow fields and engineering performance metrics under representative operating conditions, allowing equipment manufacturers to validate new designs before commercialization and objectively demonstrate their performance to customers. Instead of relying solely on simulations or empirical correlations, manufacturers can support performance guarantees with experimentally validated hydrodynamic data, reducing technical and commercial risk while increasing customer confidence. Typical applications include validating mixing performance, pumping capacity, gas dispersion, oxygen transfer, heat transfer, solids suspension, and other application-specific performance criteria.

Engineering firms are frequently asked to make critical design recommendations with limited experimental data and limited time. Fluidmapper provides objective hydrodynamic measurements that complement CFD, process models, and engineering judgement. This enables engineering firms to validate assumptions, compare design alternatives, troubleshoot existing equipment, and provide recommendations supported by experimental evidence rather than simulation alone. The result is greater confidence in engineering decisions, reduced project risk, and higher-value engineering deliverables.

Yes. Fluidmapper generates traceable experimental datasets that can support engineering documentation, process validation, technology transfer, equipment qualification, and regulatory submissions where objective evidence of hydrodynamic performance is required. While Fluidmapper does not certify equipment or processes, it provides experimentally measured data, validation reports, uncertainty quantification, and engineering documentation that can strengthen qualification packages and support Quality by Design (QbD), Good Manufacturing Practice (GMP), FDA, and other regulated engineering workflows.

Operators often inherit equipment that does not perform exactly as expected. Fluidmapper enables operators to experimentally evaluate the hydrodynamic performance of existing equipment under actual operating conditions, identify dead zones, short-circuiting, poor gas dispersion, inadequate mixing, or other performance limitations, and determine whether operational changes or equipment modifications are justified. This reduces uncertainty when making capital investment decisions and provides objective evidence to support troubleshooting, optimization, or retrofit projects.

Yes. Fluidmapper provides independently measured hydrodynamic datasets with quantified uncertainty that can be shared with customers as objective engineering evidence. Whether demonstrating the performance of a new agitator, validating a sparger design, comparing competing configurations, or supporting a process guarantee, experimentally measured flow data helps build confidence by replacing assumptions with independently validated measurements. This can strengthen customer acceptance, reduce disputes over equipment performance, and differentiate your organization through evidence-based engineering.

Yes. One of Fluidmapper's long-term objectives is to help establish publicly available, experimentally validated benchmark datasets for widely used industrial equipment and standard reactor geometries. Many industries rely on common equipment platforms, such as standard bioreactors, stirred tanks, impellers, spargers, static mixers, and laboratory reactors, yet there are very few high-quality experimental datasets available to benchmark simulations, AI models, or new equipment designs. By generating standardized three-dimensional hydrodynamic datasets under well-defined operating conditions, Fluidmapper can help create reference datasets that can be shared across the engineering community. These datasets can serve as common benchmarks for:

• CFD solver validation
• Turbulence and multiphase model development
• AI surrogate model training and benchmarking
• Digital twin development
• Academic research and publication
• Equipment performance comparisons Establishing common experimental benchmarks enables researchers, software developers, equipment manufacturers, and process engineers to compare new methods against the same trusted reference data, accelerating innovation across the industry. Over time, we envision building one of the world's largest libraries of experimentally measured hydrodynamic datasets, creating a common experimental foundation for the next generation of engineering simulation and AI.

Potentially, yes. While Fluidmapper is currently offered exclusively as a Flow Data as a Service platform, we envision developing standardized, pre-calibrated benchtop measurement systems for selected applications in the future. For example, a compact 250 mL stirred reactor with a fixed detector arrangement, standardized geometry, and pre-trained AI reconstruction model could allow qualified users to generate high-quality experimental hydrodynamic datasets without performing a full calibration for every experiment. Such standardized platforms could enable:

• Rapid characterization of new formulations and operating conditions
• Generation of comparable datasets across multiple laboratories
• Benchmarking of CFD, digital twins, and AI models using common reference geometries
• Accelerated development of surrogate models and engineering foundation models
• Collaborative research using standardized experimental protocols By fixing the geometry and calibration, users could focus on generating experimental datasets rather than developing measurement infrastructure, while maintaining compatibility with a growing library of Fluidmapper benchmark data. Although this is not part of our current commercial offering, we believe standardized measurement platforms represent an exciting opportunity to expand access to physics-grounded hydrodynamic data and foster greater collaboration across industry and academia.

Our vision is to become the world's leading provider of hydrodynamic datasets for industrial applications. We believe there is a growing need for large volumes of high-quality, physics-grounded fluid dynamics data to support equipment design, process optimization, CFD validation, digital twins, and next-generation AI models. Over the next five years, we aim to become the preferred hydrodynamic data partner for:

• Equipment manufacturers
• Engineering and consulting firms
• CFD companies
• AI and digital twin developers
• Industrial process operators Our goal is to be recognized not only for the quantity of data we generate, but for the quality, traceability, and trustworthiness of that data. Every dataset will be supported by validation, uncertainty quantification, and standardized methodologies. We envision operating multiple standardized measurement platforms capable of generating thousands of hydrodynamic datasets annually across a wide range of industrial processes and equipment configurations Additional questions from the Conference