KUNGFU.AI helped Policy Reporter automate document analysis with custom AI models, boosting search accuracy to 93% and transforming their manual data extraction process into an efficient, scalable solution.
Policy Reporter aimed to streamline how it delivered business information to clients in the healthcare space—ranging from pharmaceutical manufacturers to market analysts and individual consumers. With access to every U.S. healthcare insurance carrier, they had a unique opportunity to provide accurate insights. But their manual search process for extracting relevant information from massive healthcare documents wasn’t scalable. To grow without hiring an army of data extractors, they needed an AI-driven solution.
Teams at Policy Reporter manually combed through extensive collections of documents to find the answers clients needed. This approach was unsustainable. As the business grew, so did the document volume, requiring more time, effort, and employees. Policy Reporter needed to automate the information extraction process to improve accuracy and reduce manual labor without compromising quality.
KUNGFU.AI tackled the problem in two phases:
Additionally, KUNGFU.AI developed a windowing method to break down and recombine large documents, making it easier to process hundreds of drug references across hundreds of pages. An elastic search tool was also implemented to help parse model outputs, enabling faster, more accurate data retrieval.
The results were transformative. Policy Reporter’s teams moved from manually searching for information to simply verifying the AI model’s output. This streamlined workflow allowed them to focus on higher-value tasks.
Key outcomes included:
By automating document analysis and focusing on skill-building, Policy Reporter set itself up for sustained growth and operational efficiency. This AI-driven approach isn’t just a short-term win—it’s a long-term game changer for how they handle healthcare data.
