Stop accepting 'human error' as a root cause. AI-powered pattern detection finds the systemic issues behind individual deviations.

"Human error" isn't a root cause. A deviation log full of "operator error" and "retrain operator" tells regulators you don't understand your processes. The question isn't who made the mistake—it's what made the mistake possible.
You log a deviation: "pH excursion during hold step, Batch 2847." Before you finish typing, AI searches your deviation history and surfaces: "12 similar deviations in the past 18 months. 8 involved the same hold tank. 6 occurred during shift change. Common root cause: calibration drift on pH probe PT-401."
You were about to investigate this as an isolated event. AI showed you it's the thirteenth instance of a systemic problem. The investigation shifts from "what happened to this batch" to "why does this keep happening."
A deviation in Seal isn't a form—it's a snapshot of everything happening at that moment. Batch ID, equipment status, operator training records, process values, environmental conditions. When an auditor asks "what happened?", you show them the complete picture. When AI analyzes the deviation, it has the same context.
Equipment sensors detect out-of-spec conditions and create deviations automatically: temperature excursion duration and range, pressure spike timestamp, alarm trigger conditions. The system captures objective data. The operator adds interpretation.
You start a 5-Why. AI suggests the next question based on your deviation history and industry patterns.
Why did the operator skip the step? AI suggests: "In 73% of similar deviations, procedure clarity was cited. The current SOP was revised 6 months ago." You investigate and confirm—the procedure was confusing. Why was it confusing? It was updated without usability review. Why wasn't it reviewed? No review process exists. Now you have something systemic to fix.
AI drafts the investigation summary: "Root cause: Procedure SOP-2847 section 4.3 lacks clear hold time specification, leading to operator interpretation variability. This is the fourth deviation linked to this procedure revision. Recommended action: Revise SOP with explicit hold parameters and operator verification step." You review, edit, approve.
Individual deviations are noise. Patterns are signal. Seal's AI continuously analyzes:
When a pattern emerges, the system alerts before you've accumulated enough deviations to notice manually. The signal that took 12 incidents to see? AI flags it at 4.
Critical deviations notify QA leadership immediately and require VP approval to close. Minor documentation gaps close with QA specialist review. AI suggests classification based on deviation details and historical outcomes—you confirm or override.
Timelines are enforced. Aging deviations escalate automatically. Nothing sits in a queue waiting for someone to remember it exists.
