Industries
Life Sciences & Chemicals
Quality must be measurablebefore it is inspected.
In regulated laboratories and production environments, data quality determines release, deviation handling and trust. alpLytics connects LIMS, MES, QC and process data into validated data paths that make quality visible earlier and support inspections with confidence.
The Reality in Life Sciences and Chemicals
Quality data exists. Decision readiness often comes too late.
Laboratory, production and quality teams create critical signals every day. Too often these signals remain in separate systems, manual review paths and documents that only come together at the end of a batch.
LIMS and MES as isolated data islands
Laboratory data, production parameters and release information are often assessed separately. This prevents one continuous view of batch status, deviations, root causes and release readiness.
ASSESS maps data flows and critical quality attributes. ENGINEER builds validated interfaces and traceable data paths.
Deviations become visible too late in review
OOS, OOT and process deviations arise during manufacturing and testing. When data comes together only during batch review, root causes are detected later and measures become more expensive.
DESIGN develops quality logic for active batches. ENGINEER implements monitoring, thresholds and role based escalation.
Audit trails exist but are rarely decision ready
Electronic records, manual interventions and data changes are often technically documented but hard to interpret. For QA and inspection, storage is not enough. Traceability matters.
DESIGN anchors ALCOA and Annex 11 requirements. ENGINEER makes lineage, versions and release paths usable.
Our Approach
Validated data paths for quality, release and inspection.
In life sciences and chemicals, every data transformation is part of a quality promise. The ADET framework connects data architecture, qualification, documentation and operations so technical implementation and regulatory traceability are built together.
Understanding quality data and regulatory risk
We analyse LIMS, MES, QC, process data and manual review paths for data quality, completeness and regulatory relevance. Interviews with QA, production, lab and IT reveal where deviations, handovers and audit risks arise.
Data and compliance audit with prioritised quality risks
Architecture for validated data flows
We design data models, interfaces and control points for traceable batch quality. Roles, approvals, audit trail, lineage and qualification logic are included from the start.
Technical concept, risk analysis, qualification plan and data model
Implementation and qualification
We build validated data pipelines, batch cockpits, OOS and OOT logic and audit ready reports. Testing, documentation and implementation follow the agreed qualification plan.
Qualified system, batch cockpit, test evidence and documentation
Handover with validation dossier
QA, production, lab and IT receive role based onboarding. The validation dossier, operating documentation and handover are structured for inspection readiness and ongoing operation.
Validation dossier, SOP documentation and four weeks of hypercare
Understanding quality data and regulatory risk
We analyse LIMS, MES, QC, process data and manual review paths for data quality, completeness and regulatory relevance. Interviews with QA, production, lab and IT reveal where deviations, handovers and audit risks arise.
Data and compliance audit with prioritised quality risks
Architecture for validated data flows
We design data models, interfaces and control points for traceable batch quality. Roles, approvals, audit trail, lineage and qualification logic are included from the start.
Technical concept, risk analysis, qualification plan and data model
Implementation and qualification
We build validated data pipelines, batch cockpits, OOS and OOT logic and audit ready reports. Testing, documentation and implementation follow the agreed qualification plan.
Qualified system, batch cockpit, test evidence and documentation
Handover with validation dossier
QA, production, lab and IT receive role based onboarding. The validation dossier, operating documentation and handover are structured for inspection readiness and ongoing operation.
Validation dossier, SOP documentation and four weeks of hypercare
Results from the Field
What changes in regulated quality processes.
LIMS, MES and QC data are consolidated automatically. QA receives a traceable data path instead of manual assembly.
Critical deviations become visible during testing and manufacturing, not only in final review.
Audit trail, lineage and qualification evidence are prepared so QA and inspection teams can review faster.
Batch information was manually assembled from LIMS, MES and QC. Deviations became visible late and QA had to trace data paths manually.
alpLytics implemented validated data integration with a batch cockpit, OOS logic and electronically traceable lineage.
QA, production and IT have a reviewable data path for batch review, release and inspection.
Quality Release Flow
From batch start to reviewable release.
The flow shows how production, laboratory and QA signals are connected so quality becomes visible earlier and release becomes defensible.
OOS and OOT Detection
Out of specification and out of trend deviations are prioritised and linked to lineage, timestamp and responsible role.
Relevant Services
What we bring to life sciences and chemicals.
Next Step
Which release still depends on manual data work?
In 30 minutes we identify where LIMS, MES, QC or process data are not yet decision ready and which validated automation would create impact first.