Skip to content
DigitalNeuron
도구·제품

AWS details IskayPet's corporate knowledge assistant built on Amazon Bedrock Managed Knowledge Bases

AWS published a case study on how pet care retailer IskayPet built a multilingual corporate knowledge assistant using Amazon Bedrock Managed Knowledge Bases.

DigitalNeuron Desk2분 읽기

한 줄 답

What did AWS announce about IskayPet's use of Amazon Bedrock Managed Knowledge Bases?

AWS published a case study describing how IskayPet, a pet care retailer operating in Spain, Portugal and France, built a corporate knowledge assistant on Amazon Bedrock Managed Knowledge Bases. The assistant answers employee questions in four languages by retrieving and citing passages from IskayPet's existing SharePoint documents, using hybrid search, reranking, and document-level permissions.

핵심 요약

  • AWS published a blog post describing how IskayPet, which operates more than 330 stores in Spain, Portugal and France under brands including Kiwoko and Tiendanimal, built an internal knowledge assistant using Amazon Bedrock Managed Knowledge Bases.
  • According to AWS, the assistant answers employee questions in Spanish, Portuguese, French and English across retail operations, veterinary care, people and onboarding, and procurement.
  • AWS states the solution uses a native SharePoint connector to ingest IskayPet's existing documents without a data migration, and applies hybrid semantic-and-keyword search plus managed reranking at retrieval time.
  • AWS says the knowledge base enforces SharePoint's existing document-level permissions during retrieval, so answers are restricted to documents each employee is already allowed to read.
  • AWS reports that IskayPet's team built the full solution, including the frontend, architecture, and deployment, using Kiro with Claude Haiku 4.5.

What AWS announced

AWS published a blog post describing how IskayPet, a pet care retailer operating under the Kiwoko, Tiendanimal, Kivet, Clinicanimal and Animalis brands across Spain, Portugal and France, built an internal knowledge assistant using Amazon Bedrock Managed Knowledge Bases.

According to AWS, IskayPet runs more than 330 stores and employs more than 4,500 people serving 7 million customers. AWS says the company's operational knowledge, covering return policies, veterinary protocols, onboarding steps and procurement procedures, was spread across hundreds of Microsoft SharePoint documents in four languages, and that finding the right document took employees significant time.

The architecture AWS describes

AWS states that IskayPet selected Amazon Bedrock Managed Knowledge Bases specifically to avoid operating vector databases, embedding pipelines and ingestion jobs directly. AWS lists several capabilities it says drove the decision:

  • A native SharePoint connector that AWS says ingests documents directly from IskayPet's existing environment, avoiding a data migration, and processes content from images embedded in documents when advanced indexing is enabled.
  • Built-in hybrid search, combining semantic and keyword (BM25) search, which AWS says recovers exact terms such as product codes that pure semantic search can miss.
  • Managed reranking of retrieved passages, which AWS says is included at no additional charge.
  • Document-level access control, which AWS says enforces SharePoint's own permissions at retrieval time so a query returns only passages from documents the requesting employee is already allowed to read.
  • Model flexibility, letting IskayPet change the underlying model later without rewriting infrastructure, according to AWS.
  • Built-in RAG evaluation tools, which AWS says the team used to measure quality.

AWS describes the resulting flow as follows: an employee submits a question through a chat interface; the application calls the knowledge base for the relevant assistant, passing the employee's identity so permissions apply to retrieval; the knowledge base runs hybrid search, reranks results, and returns passages with source references; a foundation model then generates an answer from those passages, with a prompt that AWS says requires source citation and instructs the model to state when an answer isn't in the retrieved material.

Scope and development

AWS says the assistant answers questions across retail operations, veterinary care, people and onboarding, and procurement, in Spanish, Portuguese, French and English, and across IskayPet's three countries of operation. AWS states the goal was for the system to route each question to the correct body of knowledge without cross-contamination and to scale to new areas or countries without requiring a re-architecture.

AWS also states that IskayPet's team built the complete solution, including the store-facing frontend, the AWS architecture, and the deployment, using Kiro with Claude Haiku 4.5.

Source: AWS Machine Learning Blog, "How IskayPet unified corporate knowledge with Amazon Bedrock Managed Knowledge Bases," published October 22, 2026.

자주 묻는 질문

What problem was IskayPet trying to solve?
According to AWS, IskayPet's office staff had to manually search hundreds of SharePoint documents across four languages and three countries to answer operational questions, a process AWS says was slow and often returned outdated information.
What AWS service powers the IskayPet assistant?
AWS says IskayPet built the assistant on Amazon Bedrock Managed Knowledge Bases, which AWS states handles data ingestion, embedding generation, vector storage, and retrieval without requiring IskayPet to operate its own RAG infrastructure.
How does the assistant handle permissions?
AWS states the knowledge base applies document-level access control at retrieval time, enforcing IskayPet's existing SharePoint permissions so a query only returns passages from documents the requesting employee can already access.
What tools did IskayPet use to build the solution?
AWS says IskayPet's team built the entire solution, from the frontend to the AWS architecture and deployment, using Kiro with Claude Haiku 4.5.

출처

  1. How IskayPet unified corporate knowledge with Amazon Bedrock Managed Knowledge Bases | Artificial Intelligence — Amazon Web Services (AWS)
태그amazon-bedrockawsknowledge-basesragiskaypetgenerative-aienterprise-ai

함께 읽기

AWS, 아마존 폴리 생성형 엔진에 신규 음성 14종 추가

AWS는 아마존 폴리 생성형 엔진의 정식 업그레이드를 발표하며 9개 로케일에 걸쳐 신규 음성 14종을 추가했다고 밝혔다. 이번 업그레이드로 힌디어와 핀란드어 지원이 처음으로 도입됐으며, 전체 생성형 음성 수는 11개 언어, 25개 로케일에서 총 57종으로 늘어났다. 기존 사용자는 코드나 가격 변경 없이 그대로 이용할 수 있다.

2분 읽기

AWS, 세이지메이커 AI에 다중 턴 강화학습 도구 공개

AWS는 아마존 세이지메이커 AI에서 다중 턴 강화학습(MTRL)을 설명하는 기술 가이드를 공개했다. 이는 서버리스 파이프라인으로, 아마존 베드록 에이전트코어를 활용해 에이전트와 보상 함수를 호스팅하고 개별 프롬프트가 아닌 전체 행동 시퀀스를 평가함으로써 다단계 에이전트 작업에 대해 모델을 훈련시킨다.

3분 읽기

AWS, 에이전틱 코딩용 리플렉션 AI의 오픈 웨이트 모델 '빔' 지원 추가

AWS는 에이전틱 코딩을 위한 오픈 웨이트 전문가 혼합(mixture-of-experts) 모델인 리플렉션 AI의 빔을 아마존 세이지메이커에 배포하는 기술 가이드를 공개했다. 빔은 총 5,010억 개의 파라미터와 230억 개의 활성 파라미터를 가지며, 토큰 컨텍스트 길이는 100만 개에 달한다고 알려졌다. 가중치는 아파치 2.0 라이선스로 허깅페이스에 공개됐다.

2분 읽기