Questions about scientific intelligence

Shwin is a scientific intelligence engine that connects the world's science across healthcare, drug discovery, agriculture, veterinary and environmental research. It turns fragmented evidence into trusted, cited, cross-domain intelligence — delivered as apps, an API and a platform others build on.

General AI is impressive but invents facts and citations exactly where it matters most, works in a single domain, and cannot show its evidence. Shwin is built the opposite way: every claim is grounded to a real source (cite-by-index), it connects evidence across domains, it has a scientific memory that compounds, and it reports verification, confidence and coverage honestly.

Shwin retrieves from verified, multi-source scientific evidence and grounds every claim to a real citation. A dedicated verification layer checks that outputs are supported and flags coverage gaps rather than guessing — so answers are evidence-backed and auditable, and a human always makes the final decision.

A knowledge graph links scientific entities — genes, drugs, diseases, crops, pathogens, chemicals, papers — and the relationships between them across domains. Shwin uses this to connect evidence that no single tool or discipline connects, enabling cross-domain reasoning.

Healthcare, drug discovery and biomedical research, agriculture, veterinary and animal health, environmental science, and public health — powered by one cross-domain engine.

Yes. Shwin connects targets, pathways and clinical-trial evidence across the literature to surface drug-repurposing candidates with graded, cited support — helping researchers prioritise hypotheses grounded in verifiable evidence.

A clinical copilot provides guideline-backed decision support inside a clinician's workflow — surfacing options, interactions and contraindications with citations. A diagnostic assistant reasons over symptoms, labs and imaging notes to rank differential diagnoses with the evidence behind each. Both are support tools; the clinician always decides.

A patient digital twin is a living, evidence-grounded model of an individual — fed by records and wearable signals — used to simulate likely treatment response and flag early deterioration, so care can be more proactive and personalised.

Shwin builds a digital twin of the farm, fusing field, satellite and agronomic science to detect crop disease and pests early, forecast yield and guide action — connecting data sources that are usually siloed.

Shwin models ecosystems, pollution and biodiversity to forecast environmental and resource risk, and runs population-scale surveillance that fuses clinical, environmental and genomic signals to flag emerging outbreaks before they spread.

Shwin is a support tool. Every output is evidence-backed and auditable, and a human always makes the final decision — especially in high-stakes settings like healthcare.

Shwin is available for early access. Request access through the contact form on shwinlabs.com and tell us your domain and use case — it is delivered as apps, an API and a platform others can build on.