AI-native applications, open-source tools, and proprietary platforms built at the intersection of artificial intelligence, health technology, and data infrastructure. We engineer solutions where the gap between what machines can do and what institutions need is widest.
Building production AI systems that go beyond prototypes. Fine-tuning, RAG architectures, agent frameworks, evaluation pipelines, and deployment infrastructure for large language models in enterprise and health contexts.
End-to-end application development from database architecture to frontend interfaces. Modern web applications, APIs, microservices, and data platforms built with production-grade reliability, security, and performance.
Scalable data pipelines, feature stores, model training infrastructure, and deployment automation. From raw data ingestion to production model serving, we build the infrastructure that makes machine learning operational.
Purpose-built software for clinical research, epidemiological surveillance, and health system analytics. FHIR-compliant data platforms, clinical decision support tools, and health data interoperability solutions for LMIC and institutional contexts.
Building and maintaining open-source tools for the global developer community. Statistical computing libraries, AI utilities, health data standards, and developer tooling released under permissive licences for maximum adoption and community contribution.
Cloud architecture, infrastructure-as-code, container orchestration, and platform engineering. AWS, GCP, and hybrid cloud deployments with security-first design, cost optimisation, and automated scaling for production workloads.
We believe the best software is built in the open. Our open-source contributions span statistical computing, AI tooling, and health data infrastructure. We release under permissive licences because adoption matters more than control.
Projects are published on GitHub as they reach production readiness. Watch this space.