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提高效率:Pilot 如何利用向量存储、数据质量和 GenAI 实现商业价值.pdf

上传人: Fl****zo 编号:718935 2025-06-22 22页 3.16MB

1、Fueling EfficiencyHow Pilot Uses Vector Stores,Data Quality,and GenAI to Deliver Business ValueTravis Lawrence|Pilot Travel CentersAbout Pilot Travel CentersLeading fuel supplier and largest travel center operator in North America850 locations in 44 US States and six Canadian ProvincesDrop a load of

2、 fuel every 22 secondsThird largest tanker fleetServe professional drivers and travelersDeveloping coast to coast EV charging network and installing hydrogen stationsStock brand photo for Pilot Travel Centers3The Bills of Lading Processing Challenge300+different BOL layoutsCell phone photos with var

3、iable quality and orientation issuesMix of typed,printed,and handwritten contentHigh volume requiring significant manual process4Diversity of informationThe Bills of Lading Processing Challenge300+different BOL layoutsCell phone photos with variable quality and orientation issuesMix of typed,printed

4、,and handwritten contentHigh volume requiring significant manual process5Diversity of informationThe Bills of Lading Processing Challenge300+different BOL layoutsCell phone photos with variable quality and orientation issuesMix of typed,printed,and handwritten contentHigh volume requiring significan

5、t manual process6Diversity of InformationBOL Processing:Previous State7Human CentricScaling a Maintainable GenAI SolutionOur Solution JourneyOur First Automation AttemptOCR errorsImage quality/orientation issuesRandom placement of handwritten elements Required over 300 templates9Why Traditional RPA

6、FailedFirst GenAI Attempt10Overly OptimisticFirst GenAI AttemptOCR errorsPrompt engineering became unwieldyEdge cases multiplied with each new variantNot sustainable to handle all variations in the prompt 11Overly Optimistic First GenAI AttemptOCR errorsPrompt engineering became unwieldyEdge cases m

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本文介绍了Pilot Travel Centers如何利用Vector Stores、DataQuality和GenAI技术,提高业务价值。关键点如下: 1. Pilot Travel Centers是北美领先的燃油供应商和最大的旅行中心运营商,拥有850个地点,为专业司机和旅客提供服务。 2. 面临的挑战:处理超过300种不同格式的提单(BOL),其中包括手机照片、不同质量的手写、打印内容,需大量手动处理。 3. 初次自动化尝试因OCR错误和图像质量问题失败;后续采用少样本提示方法改进,降低提示复杂性,但仍有挑战如OCR质量和管道复杂性。 4. 采用多模态少样本和模糊匹配方法进一步优化,通过Delta表存储向量,监测文档类型漂移,实现自动化警报。 5. 业务影响:员工转型,99%的手动文档处理减少,数据录入积压消除,总体处理成本降低90%,收入增加,且解决方案具有可扩展性。 核心数据:处理300+种BOL格式,实现99%的手动处理减少,90%的处理成本降低。
"如何实现99%的手动处理削减?" "Pilot Travel的AI转型秘诀是什么?" "哪些技术让Pilot处理效率飞跃?"
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