Science & Engineering
Make difficult systems observable.
We develop intelligent sensing and decision systems for problems where direct observation is difficult, expensive or impossible.
Our software sits between sensing hardware and operational decisions: transforming measurements into estimates, quantifying what is known and uncertain, and helping determine what should be measured next.
Technology platform
From measurements to decisions
We are building a modular intelligence layer for sensing and inspection systems. Panoplytics software combines measurements with physical models to reconstruct what cannot be observed directly, quantify confidence in the result, and determine what information should be collected next.
The approach is designed to work with different sensing technologies and hardware platforms, allowing the same underlying decision architecture to be applied across different physical problems.
Interactive demonstrator
See the loop in action.
A hidden feature sits somewhere inside a simple two-dimensional region. Each measurement updates a Bayesian belief about its location. The current uncertainty is then used to identify the measurement expected to provide the most new information.
Follow the recommendation, or choose a sensing position and band yourself.
Interactive demonstrator unavailable. The concept is: measure, update the inferred state and its uncertainty, then choose the next measurement expected to be most informative.
Suggested next measurementCalculating recommendation…
Expected information—
Measurements0
Uncertainty reduced0%
90% search area90%
Expected information: —
No measurements yet. The current belief is deliberately broad.
What we currently believe
Where to measure next
How this toy model works
The unknown feature is represented by 1,025 possible locations. Each virtual measurement has a different spatial sensitivity and returns a noisy synthetic signal. Bayes' rule updates the probability assigned to each possible location.
For every possible next sensing position and band, the demonstrator evaluates the expected information gain: how much that observation is expected to reduce uncertainty before the measurement is taken. The highest-value measurement is then recommended.
This is deliberately a simple model. The same decision loop can sit around much more sophisticated physical models and sensing systems.
Illustrative toy model. This demonstrator uses a simplified synthetic sensing model to explain the adaptive measurement concept; it is not a performance claim for any particular sensing technology.
Hardware agnostic
The sensing platform can change without changing the overall decision architecture.
Physics informed
Measurements are interpreted using models of the underlying physical system.
Uncertainty aware
Outputs include confidence in the inferred result rather than only a point prediction.
Adaptive
The current estimate and its uncertainty can be used to select the next most useful measurement.
One connected capability
Built as one decision architecture.
Each part strengthens the others: better observations support better estimates, and better estimates make subsequent observations more useful.
Sensing & experimental design
Determine what to measure, where to measure it and which observation is likely to be most informative.
Inference & reconstruction
Transform indirect measurements into estimates of hidden physical quantities, objects or system states.
Uncertainty & assurance
Quantify confidence, distinguish ambiguity from evidence and identify when further information is needed.
Scientific decision systems
Combine sensing, modelling, inference and uncertainty into software that supports interpretation and subsequent action.
Application areas
Problems where observation is not straightforward.
Characterising inaccessible systems, optimising measurements, locating or identifying unknown features, and supporting decisions when observations are sparse, indirect or expensive.
Nuclear & hazardous environments
Making indirect or constrained observations more useful for expert teams.
Sensing, inspection & characterisation
Connecting measurement choices to the information needed for a decision.
Healthcare & medical technology
Supporting interpretation where confidence and uncertainty matter.
Advanced engineering systems
Helping teams work from incomplete evidence in technically demanding settings.
Engagements
Ways to work with us
We work on consequential technical problems, helping teams establish what is possible and build capability for the next decision.
Technical Sprint
A focused investigation of a difficult scientific or engineering question. We establish feasibility, identify the important uncertainties and determine the most useful next step.
Prototype Programme
We turn a promising method into a working computational demonstrator using your models, measurements or experimental system.
R&D Programme Design
We define the scientific questions, work packages, experiments and decision gates required to develop an ambitious technical capability.
Scientific & Technical Advisory
Ongoing senior scientific direction for organisations developing technically demanding products or programmes.
Future case studies
Adaptive RF inspection
Adaptive sensing can use the current reconstruction and its uncertainty to focus subsequent measurements where additional information is most valuable.
Have a technical problem?
Talk to us about the decision you need to make.