DataChaperone
DataChaperone provides a governed workflow platform that turns manual laboratory data analysis into scalable, audit-ready processes for CROs, CDMOs, and biotech teams. The platform automates data import, applies QC rules, and generates traceable reports with full data lineage, integrating with existing LIMS and ELN systems. It also offers Workflow AI for complex decisions, such as machine-learning-based flow cytometry gating.
- Artificial Intelligence
- Data & Analytics
- Biotechnology
- Healthcare Technology
- Software Only
Funding
Founders
Product
Problem
Laboratories in the life sciences sector, including CROs, CDMOs, and biotech teams, rely heavily on manual, fragmented workflows to transform raw assay data into decision-ready results. This dependence introduces subjectivity, variability, and a significant error rate, while repetitive analyses and reporting can consume up to 25% of scientists' time, creating a bottleneck that limits scalability and efficiency.
Solution
DataChaperone provides an objective, traceable analysis layer that captures analysis logic and turns it into governed, repeatable workflows. The platform automates data import, transformation, and quality control, pulling relevant metadata from a lab's LIMS and applying agreed calculations and QC rules to produce review-ready results in the desired format or push them to the ELN. It integrates with existing systems and processes, allowing labs to scale data analysis without disrupting their current operations. The solution is implemented through a pragmatic approach, starting with a Workflow Scan and delivering a production-ready workflow within 4-6 weeks, with options to extend to Workflow AI for complex decisions and meta-analyses across studies.
Target Audience
Primary customers are CROs, CDMOs, and biotech teams that have strong science and basic digital tools but still depend on manual analysis, including roles in analytical, R&D, QC & production, bioinformatics, and QA & validation.
Features
- Governed workflow automation that standardizes routine assays, reducing manual work and ensuring consistent results across teams and sites.
- Full data lineage and audit trails, with method and model versions recorded for every result, making audits routine and non-disruptive.
- Workflow AI module that adds intelligent logic for complex decisions, such as detecting patterns, classifying peaks or populations, and augmenting decision-making, with traceable and validated model versions.
- Integration with existing LIMS, ELN, and instrumentation systems, allowing for seamless data flow and reporting without replacing current platforms.
- Meta-analysis capabilities that enable learning from standardized results across runs, batches, or sites.
- Clean, reliable data outputs for bioinformatics and data science pipelines, ensuring models can run in production without constant rework.