-
Our research generates significant volumes of written output organized to facilitate discovery and analytical exploitation. These are the intellectual building blocks of our work: primary sources and field data, cleaned, collated and coded, processed and bundled into machine-readable formats.
-
-
Our seedpacks are the foundational unit of a defensible AI training or configuration workflow. Each seedpack matures a semantic object into a working, coherent, machine-readable corpus - including a design file of distilled fundamentals, source registry, evidence catalogue, controlled vocabulary, narrative examples, causal traces. We maintain a working library of reusable and updatable seedpacks, and we use them as baseline components of our evidence control mesh, ontology design, and more practical applications such as proposal development, market studies, competitor intelligence, and decision briefs.
-
Case studies are essential components of our seedpacks and a vital resource for applied learning and causal reasoning. We maintain a growing reference library of developed cases, available as separate resources and presented in traditional report formats or as full-text pages with wiki-style navigation. Sitting behind the library is a case registry that records dispositions, evidence, screening events, and authorisations as append-only records, linked to their supporting documents.
-
At the heart of our R&D portfolio is an enduring commitment to data, and its traces, applications, authority and affordances. Developing and maintaining datasets is a natural feature of evidence-based, intelligence-led work, and our research generates meaningful, reusable information resources. We take nothing about it for granted as we design and build the tools we need to make sense of it. Not all data can be retained or claimed as proprietary, and provenance tracing and adherence to standards of evidence plays a major role in how we work.