Enterprise Knowledge Search
Empolis wanted to enhance its knowledge-management SaaS products so users no longer had to manually search through manuals and operating procedures for answers, troubleshooting guidance, and product information.
Reported impact
Empolis created a generative AI assistant that lets users search and query millions of documents conversationally, helping customers such as KUKA troubleshoot machinery faster, learn more about their equipment, and prevent operational issues.
Empolis extended its knowledge-management SaaS portfolio with a generative AI assistant called Empolis Buddy by building on Amazon Bedrock. The assistant is integrated into existing Empolis products and can query millions of documents, including standard operating procedures and manufacturing manuals, to surface precise answers for end users. Empolis developed the offering with AWS and validated it in collaboration with customer KUKA, using Bedrock to access large language models without managing the underlying model infrastructure itself. This architecture lets the assistant sit on top of large enterprise document corpora and deliver advanced search, troubleshooting, and operational guidance in natural language. The result is a reusable generative AI knowledge layer that helps industrial users learn faster, resolve issues sooner, and prevent operational problems.
Millions of manuals, standard operating procedures, and product-information documents are queried through Empolis Buddy, which uses Amazon Bedrock to turn document content into natural-language answers for end users.
Empolis used Amazon Web Services (AWS) to develop a virtual assistant – known as the Empolis Buddy – powered by large language models on Amazon Bedrock.
The generative AI assistant can access and query millions of documents, such as standard operating procedures and manufacturing manuals.
This ability helps users learn more about their machinery, troubleshoot problems faster, and prevent operational issues.
Together with its customer KUKA ... Empolis formulated a proof of concept for a generative AI assistant in April 2023.
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
According to the latest account statement, there are currently 189 of employees working for Empolis Information Management GmbH.
Related
AWS Sample Feb 14, 2025 MANZ MANZ , an Austrian company that provides information services for legal and tax professionals, sought to revolutionize how its registered users searched its vast repository of legal documents. 30% expected savings Amazon Neptune · Amazon OpenSearch Service AWS Sample Jan 28, 2025 Bynder Bynder initially acquired a company to incorporate visual search capabilities into its software. The fast rate of customer adoption encouraged Bynder to build on its success. One of Bynder’s customers states that the time spent searching for assets for a typical campaign task has… Amazon Bedrock · Amazon Titan Multimodal Embeddings AWS Sample Dec 11, 2024 Goodnotes Founded in 2011, Goodnotes has been a leader in digital note-taking and productivity, now serving over 24 million monthly active users. As a pioneer in digital handwriting, Goodnotes was among the first apps to support… Founded in 2011, Goodnotes has been a leader in digital note-taking and productivity, now serving over 24… Amazon Bedrock · Amazon CloudWatch · Amazon EKS 