Four services, built around records you're already generating.
Nothing here requires a new system of record. We work with the batch documentation, deviation logs, and historian data your site already maintains for compliance.
Batch Record & Deviation Analytics
Statistical analysis across batch production records and deviation reports to surface patterns no single record would reveal on its own — which process steps deviate most often, which equipment lines correlate with which failure modes.
- Cross-batch deviation frequency and correlation analysis
- Equipment and shift-level pattern detection
- Findings delivered in formats your quality unit can act on directly
Continued Process Verification (CPV) Dashboards
Ongoing statistical monitoring of validated processes against critical process parameters and critical quality attributes, built to support your Stage 3 process validation obligations under the FDA's process validation lifecycle.
- Control charts against defined CPP and CQA limits
- Automated flagging of out-of-trend results before they become out-of-specification
- Periodic review reports formatted for your quality management system
Data Integrity & ALCOA+ Audit Trail Review
Systematic review of electronic batch records and audit trails against ALCOA+ principles, catching the shared-login, missing-timestamp, and incomplete-field issues that show up repeatedly in FDA warning letters.
- Audit trail review against ALCOA+ criteria
- 21 CFR Part 11 electronic records gap assessment
- Remediation prioritization by inspection risk
OOS/OOT Investigation Support & Trending
Trending of Out-of-Specification and Out-of-Trend results over time to distinguish an isolated lab or process event from a systemic issue building across multiple batches.
- Historical OOS/OOT trend analysis by product and line
- Statistical support for investigation impact assessments
- Early-warning thresholds set below formal OOS limits
We don't perform root cause investigations or sign off on CAPA.
We identify statistically meaningful patterns in your data. Determining what caused them, and what corrective action is appropriate, is a quality and process engineering judgment that belongs with your team — we support that work with better evidence, not a substitute for it.