SciSure and DataChaperone serve many of the same teams: scientists working in regulated or quality-conscious environments, where reproducibility, traceability, and scalability matter. In this partnership, the two platforms remain clearly complementary. SciSure focuses on experimental design, data capture, and documentation, while DataChaperone provides an automated analysis and reporting layer on top.
Through the SciSure–DataChaperone partnership, users can move from experimental data captured in SciSure directly into governed, automated analysis workflows in DataChaperone, and return results and reports back to SciSure for documentation, review, and sharing. This creates a closed loop from experiment to analysis to final record, without manual handovers, ad hoc scripts, or copy-paste steps.
For labs, this partnership enables:
-Scalable analyses that are applied consistently across experiments and users
-Full traceability from raw data to reported results
-Deploying custom AI solutions
In addition to code- and rule-based automation, DataChaperone now offers the implementation of custom AI models to SciSure users. These models are used for analysis steps that require expert judgment and cannot be fully captured otherwise. Embedded into governed workflows, AI-driven decisions remain traceable, reviewable, and aligned with quality and regulatory expectations.
The partnership reflects a shared view between SciSure and DataChaperone: improving reproducibility and data integrity does not require replacing existing systems, but connecting them more intelligently. By embedding standardized analysis and AI-assisted decision logic directly into the ELN workflow, teams can reduce bottlenecks while staying aligned with quality and regulatory requirements.
The DataChaperone add-on is now available via the SciSure Marketplace.
