Hapag-Lloyd enhances corporate audit efficiency with GenAI on Databricks
German shipping company Hapag-Lloyd fine-tuned Databricks' open-source DBRX model and built a RAG chatbot to automate audit finding generation and executive summaries, cutting review time per finding by 66% and executive summary review time by 77%.
Overview
German shipping company Hapag-Lloyd fine-tuned Databricks' open-source DBRX model and built a RAG chatbot to automate audit finding generation and executive summaries, cutting review time per finding by 66% and executive summary review time by 77%.
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Inspect the highlighted sourceThe challenge
Hapag-Lloyd's corporate audit process involved several manual instances of documentation and report writing that were time-consuming and led to inconsistencies, and its existing infrastructure, including an AWS SysOps account, did not support rapid setup and deployment of the AI models needed for audit optimization.
The solution
Hapag-Lloyd fine-tuned Databricks' open-source DBRX model on 12 trillion tokens of curated data to build a Finding Generation Interface that generates audit findings and summaries, used Databricks MLflow to automate the evaluation of prompts and models across the ML lifecycle, and built a chatbot using Gradio integrated with Databricks Model Serving that uses retrieval-augmented generation (RAG) to let auditors query documents in natural language.
Reported business value
Auditors now spend only 5 minutes creating a new finding from bullet points, down from 15 minutes, a 66% decrease in review time per finding, and the time required to review each executive summary decreased 77%, from 30 minutes to just 7 minutes.
Sources
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