What happens when the chemistry you care about most isn't in any dataset?

What happens when the chemistry you care about most isn't in any dataset?

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For most teams, data comes from two places: internal historical campaigns or the literature. When those are fragmented, inconsistent, or simply don't cover the transformation in front of you, every campaign starts from square one.

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Over the past year, we solved this for our own platform - generating 25,000+ high-quality data points across Suzuki, amide, and Buchwald-Hartwig couplings in our HTE lab, used to pre-train the reactivity models inside ReactWise.

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Now we're offering that same capability directly: HTE Data on Demand.

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We design and run large-scale experimental campaigns built around your chemistry - capturing every condition, every metadata field, and every outcome, including the negative results that most datasets throw away.

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There are two ways to use it:

→ Data as a service - a structured, machine-readable, and FAIR dataset, delivered in open ORD format, to be used across your workflows, or

→ Validated, production-ready reactivity models - trained on that data and ready to deploy.

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Running our own HTE program has shown us how hard this is to do well - miniaturization, material incompatibility, and analytical throughput are all genuine hurdles. That hard-won experience is exactly what we bring when generating data for others.

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Find out more here.