We built this because researchers deserve AI infrastructure, not more tools.

Researchers accumulate a huge amount of knowledge over their careers, in the papers they read, the studies they run, the collaborators they work with, the grants they apply for. The problem is that this knowledge lives in scattered places and in incompatible formats. When a new task starts, researchers begin again from scratch, manually reconstructing context that already exists somewhere. It's one of the most consistent and least necessary sources of wasted time in research, hours of mechanical reconstruction that displace the intellectual work that actually advances knowledge.

We started building m3t because we kept seeing the same friction across different workflows. Literature review, systematic review, grant applications: each feels like a separate problem, but they all share the same root cause. The knowledge a researcher has built up isn't connected to the work they're doing next. A researcher who has just reviewed a literature knows exactly which gaps exist and which methods are missing. But when they go to write a grant proposal, none of that intelligence is there waiting for them.

m3t's approach is to build the semantic layer underneath a researcher's work, extracting structured knowledge from papers, linking it to their profile, their collaborators, their funding history, and put that intelligence to work across every workflow. Not a bundle of tools that happen to share a login, but a shared foundation that makes each workflow more useful than it would be alone. We're building on top of systematic reviews and grant discovery, with the architecture to extend into every part of the research lifecycle. We're working with early stage research teams across the UK, US, and Europe. If you're interested in shaping the product, reach out.