about
What makes us different
Different problems need different ways of seeing them. Our work draws on a common foundation across behaviour, systems, AI and data.
Panalogy is deliberately multidisciplinary. We choose methods around the problem rather than forcing every problem through the same technology.
What we bring
- Real behaviour
- We study decisions, attitudes and interactions in context, grounding models in empirical questions about people and organisations.
- Scientific & ethical rigour
- We make assumptions explicit, test against evidence and examine the limits and consequences of what we build.
- Usable systems
- We connect research to tools that fit real workflows, with explanations and human judgement built into their use.
- Alignment in context
- We evaluate whether an AI system serves the needs of the people using it and the environment in which it operates.
Our methods
- Behavioural science & experimentation
- Understanding how people perceive, decide, respond and interact, and how those behaviours change with context.
- Systems & complexity
- Looking at relationships, feedback, emergence and interdependence rather than treating components in isolation.
- Morphological analysis
- Representing complex phenomena through meaningful combinations of characteristics, then examining how those configurations differ across situations.
- Networks & graph intelligence
- Studying people, objects and concepts through the structure of the relationships connecting them.
- AI, machine learning & agentic systems
- Developing computational systems that can predict, retrieve, generate, reason and interact.
- Simulation & synthetic populations
- Using computational agents and populations to explore behavioural variation, interaction and possible scenarios.
- Data science & quantitative analysis
- Statistical modelling, machine learning, spatial and temporal analysis, and bespoke computational methods for extracting useful structure from data.
How we usually work
Understand
Define the problem, context and decision that actually matters.
Structure
Identify the information, relationships, behaviours and constraints that need to be represented.
Explore
Use research, analysis and rapid experimentation to test promising approaches.
Build
Turn the useful parts into a working prototype, analytical system or product.
Test & learn
Evaluate it in context and refine what does — and does not — work.