[lcus_masonry_article]<\/p>\n
The Alchemy Probability Data product underpins the cure discoveries and the Chemical Cooker; it is also offered as a standalone handoff. The product insight is that you can buy the data layer without buying an application product. Millions of element and compound combinations, methods, probabilities, documentation, and RAG-ready assembly\u2014so that in-house or partner systems can query, rank, and prioritize without rebuilding the underlying methodology.<\/p>\n
What to ask.<\/strong> (1) What is the scope of the database (combinations, methods, probabilities) and in what formats? (2) Is there a RAG-ready or AI-ready assembly for your workflows? (3) How does the data relate to the other products (cure discoveries, Chemical Cooker)\u2014same methodology, so you can add those later if needed? (4) What are the terms: one finished product copy; IP not transferred unless separately agreed? (5) What documentation and support exist for integration?<\/p>\n Why it matters.<\/strong> The real-life case study showed a discovery group integrating the database into their RAG and discovery pipelines and using it as a standalone asset\u2014they did not take the Chemical Cooker or cure products in that engagement. The insight is that the data layer has value on its own for drug discovery, materials discovery, and synthesis planning, and that the same methodology appears across the portfolio for consistency.<\/p>\n Takeaway.<\/strong> Evaluate the product on database scope, integration path (RAG, query, ranking), and documentation. Treat it as a complementary object to application products: you can start with data only and add applications later, or you can use the data to power your own pipelines. The value is a complete, documented asset that can drive discovery across multiple domains.<\/p>\n View the full portfolio of 18 technology products \u2192<\/a><\/strong> [\/lcus_masonry_article]<\/p>\n <\/p>\n
\nCompanion piece: Case Study \u2014 Alchemy Probability Data<\/a><\/em><\/p>\n