Arrive Health, a pharmacy benefits manager, relied on highly manual, specialist-intensive processes to review FDA drug sheets and identify cost-equivalent drug therapies.
Specialists would read complex pharmaceutical PDFs, compare drug equivalencies, and manually enter data into a rules engine, a slow, expensive process that limited how quickly the company could surface cost savings for insurers, pharmacies, and patients. Scaling this review process without proportionally scaling headcount was the core challenge.
Artium built a RAG-based generative AI system that preprocesses FDA pharmaceutical PDFs, extracts and structures drug information, and generates drug equivalency recommendations for human specialist review, using a proprietary Continuous Alignment Testing (CAT) framework to prevent hallucinations.
Artium delivered a working AI-powered drug equivalency recommendation system for Arrive Health in approximately 10 weeks of hands-on development, compressing what would previously have been a multi-year initiative into a single quarter.
By automating the preprocessing of FDA drug sheets and generating equivalency recommendations before specialist review, the system significantly reduced manual data processing burden, allowing specialists to focus their time on validation and high-judgment decisions rather than initial data extraction.
Artium's Continuous Alignment Testing framework ensured the AI system maintained required accuracy thresholds (~95%+ for effective specialist use) in a healthcare context with strict LLM refusal challenges, enabling Arrive Health to roll out to a beta group with confidence in system reliability.
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