IskayPet Unifies Corporate Knowledge Across Four Countries with Amazon Bedrock Managed Knowledge Bases
Current overview (not historical)
IskayPet has deployed Amazon Bedrock Managed Knowledge Bases to create a unified knowledge hub that enables semantic search and generative Q&A across SharePoint documents in four languages and four countries. The solution eliminates the need for the company to manage RAG infrastructure, while leveraging Bedrock’s managed reranking and document‑level access control features to improve retrieval relevance and enforce source‑system permissions. By integrating Bedrock’s managed knowledge base, IskayPet’s employees can query corporate information in English, Spanish, French, and German, receiving context‑aware answers that respect their role‑based access rights. This approach streamlines cross‑border collaboration and reduces the operational burden of maintaining a custom retrieval‑augmented generation stack.
Written with AI from the reporting · updated 10/22/2026
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- 1Unclassified fact stationOct 23, 00:00recorded Oct 23, 02:10single source
IskayPet 用 Amazon Bedrock Managed Knowledge Bases 统一跨语言企业知识
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- Subject：
- iskaypet
- Action：
- 在 Amazon Bedrock Managed Knowledge Bases 上部署企业知识库
- Object：
- 员工问题的语义检索与生成式问答
- Occurred (fact side)：
- Oct 23, 00:00
- Published (valid side)：
- Oct 23, 02:01
- First seen (known side)：
- Oct 23, 02:10
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- AWS Machine Learning BlogFirst-party sourceRelated fact: single sourceIskayPet builds corporate knowledge assistant using Amazon Bedrock Managed Knowledge Bases
Report first recorded Oct 23, 02:10
IskayPet, a leading pet care company in Iberia operating over 330 stores across Spain, Portugal, and France with more than 4,500 employees, built a corporate knowledge assistant on Amazon Bedrock Managed Knowledge Bases. This solution addresses the challenge of finding operational answers—such as return policies or veterinary protocols—scattered across hundreds of Microsoft SharePoint documents in four languages (Spanish, Portuguese, French, and English) across three countries, eliminating the need to manage RAG infrastructure like vector databases and embedding pipelines.
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