From manual verification to controlled release
QC and production teams carry the responsibility of turning data into a pass or fail. Any delay or inconsistency in analysis directly affects release timelines, customer commitments and internal trust. When analysis depends on spreadsheets and small interpretation differences, release testing becomes unpredictable.
What users see
From manual verification to controlled release
Quality that scales with volume
As volume increases, manual review and verification quickly become bottlenecks. With DataChaperone, standardized, governed workflows allow QC capacity to scale without proportional increases in effort.
Faster, more reliable release decisions
Manual calculations and spreadsheet-based checks slow down release and increase review effort. With Workflow Automation, governed workflows automate calculations and checks, making release faster and more predictable.
Consistent method execution
Different interpretations of the same method often lead to discrepancies between analysts or sites. With DataChaperone’s Workflow Automation, standardized workflows ensure the same rules are applied every time, reducing variability and rework.
Complete, centralized documentation
Documentation is frequently scattered across instruments, folders, and email threads. Each run automatically captures data, rules, QC checks, and decisions in one traceable record that can be browsed with DataChaperone’s Meta-Analysis.
Audit-ready by default
Audits and customer reviews often require time-consuming reconstruction of past decisions. DataChaperone generates full lineage and method history, making it easy to show exactly how results were produced and answer questions from reviewers.
What our users say
“The same inputs always produce the same results and reports, independent of the operator.”
– CDMO, Associate Director QC
How DataChaperone is delivered
We focus on the workflows that matter most for your timelines and client commitments.
Instead of replacing your platforms or processes, we automate one critical workflow first. Following the Workflow Scan, we jointly define a project to implement Workflow Automation. Once we start building, you’ll have a working, production-ready solution in 4–6 weeks.
From there, we become your long-term partner: maintaining and evolving your workflows, integrating with existing systems, introducing Workflow AI, and enabling DataChaperone Meta-Analysis as you scale.
DataChaperone:
✓ Automates data import, transformation and quality control.
✓ Generates audit-ready reports with full data lineage.
✓ Deploys custom solutions with off-the shelve speed.
✓ Integrates with existing LIMS, ELN, and instrumentation.
✓ Provides meta-analysis capabilities across multiple studies.
✓ Offers custom automation to fit unique lab workflows.
DataChaperone is the objective, traceable analysis layer for modern labs.
Workflow Scan
We select a QC test and map how it runs from sample to released batch: what data is collected, how it is processed, which checks are performed and how final decisions are documented.
Workflow Automation
We encode the approved way of working into a governed workflow. Each run follows the same sequence of steps with clear QC rules and decision thresholds — no personal interpretation required.
Workflow AI
If parts of release testing involve complex interpretation (e.g., pattern detection, classification, curve evaluation), Workflow AI can support consistent decisions.
Meta-Analysis
Once core workflows are standardized, you gain reliable trends across batches, products or sites. Out-of-spec issues become visible early, and release becomes more predictable.
→ See the product overview
Start with one workflow
You don’t need a full transformation to get started.
Most teams begin with a single, high-impact workflow.
We scan it, automate it, and deliver a working result in weeks.
From there, you decide how far to scale.
Why teams start this way:
✓ See impact quickly
✓ Get buy-in across teams
✓ Validate the approach with minimal risk
✓ Free up time for higher-value work