Recommendations you can
audit and defend
Generic AI tools guess. That's fine for writing emails. It is not fine for oncology treatment decisions or drug launch strategy. Every recommendation our architecture produces traces to a specific published trial or guideline. You can ask where it came from and get a precise answer. Here is how that works.
Every recommendation traces to a source
No black boxes. Every treatment step, survival baseline, and clinical reference in our system links to a specific published trial or guideline. You can ask "where did this come from?" and get a precise, auditable answer. That's a requirement in this space, not a feature.
Drug names are matched, not guessed
Oncology data is full of aliases. MK-3475, pembrolizumab, Keytruda: the same drug, appearing differently across trials, labels, and clinical notes. Our system resolves these reliably, so nothing is misattributed or missed when we build pathways around your specific asset.
The evidence stays current
Our system continuously monitors PubMed and OpenFDA for new trial data and label updates. Pathways built on our architecture don't go stale between updates. When the evidence changes, the system reflects it, without waiting for a manual review cycle.
Want to see how this applies to your asset?
If you're a pharma or diagnostics company thinking about CDS, the architecture is only as useful as the asset it's built around. That's the conversation worth having.