Pharmaceutical quality teams face mounting deviation backlogs that delay batch release and burden investigators with administrative work. Over 70% of deviations are classified as minor, yet they often require the same procedural effort as critical cases. Siloed data, complex SOPs, and regulatory requirements (GMP, FDA, EMA, ICH) make it difficult to identify trends or reuse historical knowledge, resulting in redundant investigations and inconsistent outcomes.
To address this,Sagacify partnered with domain experts to develop a knowledge-graph-based assistant. The project focused on structuring internal quality data—deviation reports, CAPAs, SOPs—into a reasoning system that supports investigators by surfacing relevant historical cases, answering questions related to procedures, and assisting with report drafting. Designed for regulatory environments, the assistant is built around the organization’s procedures, terminology, and quality workflows.
The assistant helps quality teams handle deviations more efficiently by surfacing relevant past cases, recommending consistent actions, and reducing the time spent drafting reports. Investigators can ask questions in natural language and receive structured responses backed by internal procedures and regulatory context—without manual searches or duplication of effort.
By recognizing patterns across similar cases, the system helps teams detect root causes, suggest CAPAs, and align reports with evolving regulatory expectations. It also retains institutional knowledge, reducing dependency on specific individuals and easing onboarding.
Behind this, the solution combines a structured knowledge graph with language model reasoning. Key technical components include:
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