Contribute human samples and data to accelerate research
Joining Specie Bio as a provider is one of the easiest ways to turn your biospecimens and associated medical data into real impact. We have developed an AI-powered deep search engine to match providers and researchers for sample and data exchange - without making you reorganize your records or chase down collaborators. Our big-picture mission is straightforward: Harness the potential of untapped biological samples and multimodal medical data globally. Whether you are an academic institute, health system, cancer institute, or small clinic, Specie’s platform is built to help you share what you already have in a smarter, simpler way.
How BioExchange streamlines access to biospecimens
In preclinical research and drug development, access to high-quality biospecimens with specific inclusion and exclusion criteria remains a major bottleneck. Even the most promising projects can stall for months while teams search for suitable samples, negotiate access, and coordinate logistics.
BioExchange from Specie Bio is designed to change that. It’s an AI-driven matchmaking platform that connects researchers directly with hospitals, medical centers, biobanks, and research institutions that have the samples and data they need — all from a single search.
How Biorepositories can become more self-sustaining
In a recent survey of 456 biobanks, 71% of respondents were concerned about funding shortfalls, and 37% identified funding as their single greatest challenge. Biorepositories across academic institutions and integrated health systems often face the dual pressures of underutilized biospecimens and limited financial resources. Maintaining a biobank is resource-intensive, and traditional methods for sharing biospecimens are often inefficient. When valuable samples go unused, it’s not just funding that is lost - it’s a missed opportunity to support critical life science research.
Harness the potential of all untapped multimodal health data and samples
Like you, we are innovators and scientists. We have spent countless hours conducting benchside experiments or refining machine learning models. While those are not easy, and don’t always yield the best results we hope for, we never regret any of it.
What has drained us is not the experiment, nor the modeling, but getting the resources to even start the experiment or train the model.