Your team screened sixty conditions on a hydrogenation step. Four hit spec

Your team screened sixty conditions on a hydrogenation step. Four hit spec. Two years later, a campaign on a related intermediate starts from scratch - because the original data lives in slide decks, ELN exports, and a spreadsheet named "final_v3".

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In API process development, the chemistry is only part of the bottleneck. Everything around it slows it down: campaign data fragmented across systems, robustness studies duplicating experiments a well-designed campaign already ran, and hours lost transcribing analytical outputs between disconnected tools.

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Each of these costs is small on its own. Across a team, site, or division, they are the difference between a process that is ready for tech transfer and one that is still being argued over.

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The alternative is treating process data as infrastructure. When campaign design, optimization, and analytics live in one connected system, a different workflow becomes possible: data-driven optimization explores the design space efficiently, every result updates the model, and the trade-offs between yield, impurity, and operating conditions are visible rather than buried.

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Structured data also stops being a record and starts being a starting point. ReactWise integrates directly with your ELN and data lakes so our transfer learning algorithm, MemoryBO, can use this mine of historic data to carry forward what worked and what didn't; now a campaign on a related intermediate begins with the accumulated knowledge of everything that came before, rather than from nothing.

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Structure first, optimization second. Once the data is structured, every experiment becomes useful over and over, not just once.