Spot the risk earlier.
Resolve complaints faster.
An automated NLP pipeline identifies quality records likely to escalate, helping teams act earlier on high-risk events.
A global life sciences company
The challenge
A global life sciences company had long cycle times in its quality investigation (QI) escalation process: the time between receiving a product complaint and resolving the issue. This process is critical for spotting potential adverse events and taking corrective action to keep patients safe.
The escalation process was under growing strain, because of:
- A high volume of quality records every day
- Complex communication across teams
- Large amounts of unstructured data to review
With fast-moving global health challenges like COVID-19, delayed escalations could have severe consequences for patients, regulators and the company's reputation.
The solution
Common Sense AI developed an intelligent automation solution, powered by natural language processing (NLP), to speed up decisions in the QI process.
The system analysed historical quality records and learned to predict, at an early stage, which cases were likely to escalate. Teams could focus their attention earlier and act in time on high-risk events.
The model was embedded in a fully automated pipeline, for fast, scalable and consistent processing of every incoming record, with less manual work and better traceability.
The result
Cycle time dropped from 3–4 weeks to less than 1 week.
- Critical issues are detected earlier
- Less manual effort, thanks to intelligent triage
- A more agile, responsive QI process, crucial for product quality and public health
The project shows how targeted AI, paired with automation, can deliver measurable impact in regulated, high-stakes environments.