Here's who we already work with, and an invitation to join that list.
We're a small team working on edge AI, low-resource language processing, and clinical decision support in a low-connectivity setting. The problems we're trying to solve don't exist in isolation, and we don't pretend to have all the answers. If you're working on something adjacent (in research, in a clinic, or in the field), we'd like to talk.
Running clinical AI on low-cost Android devices with no cloud dependency. We're interested in model compression, quantization strategies for medical NLP, and runtime optimization for resource-constrained hardware. If you're working on making AI models smaller and faster without sacrificing reliability, we should talk.
Swahili is spoken by over 100 million people but remains dramatically under-resourced in NLP. Our Sema project is one approach, but there's enormous room for collaboration on Swahili clinical terminology standardization, corpus development, and evaluation benchmarks for medical Swahili understanding.
Clinical decision support in low-resource settings is fundamentally different from CDSS in well-resourced hospitals. We're interested in collaborators who understand the realities of primary healthcare in sub-Saharan Africa: how clinicians actually make decisions, what information they actually need, and how technology can support rather than complicate existing workflows.
We need to measure whether ClerQ actually improves clinical outcomes, not just whether clinicians use it. If you're working on frameworks for evaluating AI-assisted clinical decision-making in real-world primary care settings, we'd value that collaboration highly.
For regulatory authorities, health ministries, and clinical AI developers seeking auditable access to declarative STG/NEMLIT guidelines. We collaborate on deterministic rule schemas, bar-crossing confidence algorithms, and Model Context Protocol interfaces for clinical tooling.
Nama Labs is ClerQ's research partner on Sema and Rafiki. Co-founded by Emmanuel Chesco, the studio brings deep technical research in NLP architectures and edge AI systems, complementing ClerQ's clinical domain knowledge and field deployment constraints.
Both projects are open-source, and both are ongoing. The partnership model is straightforward: shared research goals, shared credit, open publication.
If any of the above resonates with your work, we'd like to hear from you. No formal process, no application form. Just tell us who you are, what you're working on, and where you see overlap.