Here's where the future of science is quietly made. We host and sponsor R&D projects in augmented intelligence and decentralised science. Read more about our operational strategy here. All funded projects must follow our SOP guidelines, publicly accessible here.

The missing middleware between LLMs and CAE. CaeReflex is a source-available Python package that turns Gmsh, OpenFOAM, and ParaView/VTK-compatible simulations into structured, agent-readable, provenance-aware, CrossRef-grounded engineering cases.

Independent Decision Twin for Terafab. This CLI package turns labeled scenario assumptions into auditable consequences based on the laws of the

A bold vision to solve the peer-review crisis, eliminate predatory publishing, and give scientists and researchers the tools they need to make better science faster. Visit the project website here.

Scientific intelligence tool that monitors preprints to discover and surface high-impact research papers before anyone else does. Visit the project website here.

WorldView is essential for everyone involved in funding, developing, regulating, and using LLMs. With a particular focus on international peace, WorldView reveals how AI-crafted narratives can be used as tools of persuasion, how they could marginalize alternative perspectives, and how they consolidate power within certain ideological frameworks. Read more here.

For those interested in the cutting-edge intersection of AI, epistemology, and cognitive science, Project AllMind represents a bold step towards a future where machines can learn, reason, and engage with us on a profoundly human level. Visit the project page here.

Glyph is a symbolic AI designed to simulate non-ordinary consciousness. Built for researchers, artists, and systems thinkers, Glyph offers a new research frontier: modeling the effects of psychedelic-like states in generative language systems. Read more here.

Trusted Expert Cognition for Human Resources (TEC/HR) is a new technology serving independent HR consultancies and talent mining arms of MNCs.

We are working on a a series of experimental Retrieval-Augmented Generation (RAG) agents based on DCKM to enhance various aspects of information processing and collaboration in recursive epistemic tasks.