As the world continues to experience the power of Artificial Intelligence (AI), many companies are eager to start implementing generative AI solutions, but not all problems are meant to be solved with the same tool set. As the AI landscape becomes increasingly crowded, differentiation at times demands more than switching on generic, off-the-shelf AI solutions.
We will talk through the challenge, decision points, and overall approach that led Farmers Mutual Hail Insurance to building our first custom AI tool. By leveraging generative AI application evaluations, metric-driven development, and a robust scaleable cloud infrastructure, FMH was able to successfully develop a custom AI solution that saved time and money, helped us differentiate ourselves in the market, and set us up for future success in AI endeavors. With this tool, new underwriters, and others with limited exposure to the field, can answer questions similarly to the most experienced underwriters at FMH, providing consistency and expanded expertise across the organization.
If you are facing a unique challenge and are evaluating build vs. buy, join us as we share our recent experience building our first custom generative AI application.
Key takeaways
1. Measuring success and determining build vs. buy.
2. How LLM RAG evaluations helped show continuous improvements throughout the project.
3. How in our project, custom AI development was able to save both time and money.
Panelists
Sheila Backer
AVP Crop Insurance Underwriting
Farmers Mutual Hail Insurance Company of Iowa
Claude Heidgerken
UX / IT Manager - Information Technology
Farmers Mutual Hail Insurance Company of Iowa
Mat Wolff
Software Developer III
Farmers Mutual Hail Insurance Company of Iowa
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