Research
Applied Research. Human-Centred AI.
We don't do research for the sake of publishing. We do it to solve problems that don't yet have a playbook.
Why We Research
In specific verticals, industries, and professional domains, we pursue applied research into how AI technologies can meaningfully augment — not replace — human expertise. The professionals we work with have spent years, often decades, building deep domain knowledge. That expertise is irreplaceable.
What AI can do is extend the reach, precision, and decision-making capacity of those professionals — processing volumes of data that no human could review alone, surfacing signals that would otherwise be missed, and compressing analysis timelines from weeks to minutes. The goal is never to automate the expert out of the loop. It's to make the expert dramatically more effective within it.
Our research agenda is shaped by client engagements, industry observation, and a conviction that the most valuable AI applications are still being designed — not by model builders in isolation, but by the people who understand the problems those models are meant to solve.
Active Research Themes
Four areas where we're investigating how AI extends professional capability.
Domain-Specific Decision Augmentation
Investigating how AI can surface patterns, anomalies, and recommendations that complement expert judgement in specialised fields — extending reach without eroding the intuition that experience built.
e.g.A seasoned agronomist who can now process satellite, soil, and weather data at a pace and granularity that was previously impossible — without losing the fieldcraft that decades of practice developed.
Knowledge Synthesis at Scale
Exploring AI-driven methods for extracting, structuring, and synthesising knowledge from large, unstructured professional corpora — turning institutional memory into accessible, queryable intelligence.
e.g.A legal team that can interrogate decades of case law and regulatory change in minutes, guided by AI but validated by human expertise — because precision matters more than speed alone.
Human-AI Collaboration Patterns
Studying how professionals interact with AI tools in practice — what builds trust, what degrades it, and how to design systems where the human remains genuinely in control rather than merely in the loop.
e.g.Clinical decision-support tools that present options and evidence rather than prescriptions, keeping the clinician’s judgement at the centre — because a recommendation is not a replacement.
Sector-Specific AI Readiness Models
Developing frameworks that assess not just technical maturity but organisational, cultural, and regulatory readiness for AI adoption in specific verticals — honest baselines, not marketing scores.
e.g.A manufacturing SME needs a fundamentally different readiness model than a fintech startup — and both deserve something more rigorous than a vendor’s self-assessment quiz.
Research that becomes practice.
Our research isn't academic in the ivory-tower sense. It's applied, field-tested, and designed to become the foundation of future service offerings and client engagements. What we learn in the lab, we deploy in the field.
Interested in collaborating?
We partner with organisations, domain experts, and academic institutions on applied AI research that solves real problems.