Global R&D teams were working across disconnected data sources and drowning in information. They needed a faster way to identify emerging trends, uncover white space, and make more confident early-stage decisions. Internal data was scattered, external signals were fragmented, and decision-making lacked the visibility and speed needed to keep up with the pace of innovation.
We began with a focused consulting project to understand how R&D teams made decisions, and where their current systems fell short. Our data science team prototyped a custom interface that brought together internal workstreams with external patent, literature, and competitive intelligence. We used clustering, NLP, and expert-led filtering to surface meaningful trends, flagging emerging areas of interest based on volume, velocity, and scientific credibility.
The prototype was piloted with active project teams to ensure it answered real questions and matched existing workflows. We continuously refined the interface, visualisation layers, and alert mechanisms based on feedback from day-to-day users.
Following the pilot’s success, we developed the prototype into a scalable enterprise software platform. The R&D Intelligence Hub now allows teams to:
The platform is being rolled out to other teams within the organisation, with ongoing development focused on additional therapy areas and use cases.
R&D teams now spend less time searching and more time making strategic decisions. By unifying external and internal intelligence into a single platform, the client can identify early signals of opportunity, reduce duplication, and ensure their research is focused on what matters most.
We continue to support rollout, training, and development of new features across the global organisation. The system is now central to how early-stage R&D is conducted, helping shape the direction of future products
