IskayPet, a major pet care company operating in Spain, Portugal, and France, has successfully deployed an internal knowledge management assistant to address operational inefficiencies. By leveraging Amazon Bedrock Managed Knowledge Bases, the company has unified documents previously scattered across hundreds of Microsoft SharePoint files, reducing the time required to retrieve information from several minutes to seconds.
Overcoming Fragmented Data
Prior to the deployment, IskayPet employees—spanning retail, veterinary, and procurement operations—relied on manual searches through documents stored in different languages and locations. This process was prone to inaccuracies and grew increasingly difficult as the company expanded its physical footprint, which now includes over 330 stores and 4,500 employees. To modernize this workflow, the internal team sought a solution capable of providing grounded, natural-language answers based on existing documentation without necessitating a massive data migration.
Implementation with Managed RAG
The company utilized Amazon Bedrock Managed Knowledge Bases to implement a Retrieval Augmented Generation (RAG) system. This approach allowed the team to bypass the complexities of managing vector databases, embedding pipelines, and ingestion infrastructure. The solution connects directly to IskayPet’s existing SharePoint repositories, utilizing advanced indexing to process text and embedded images from documents such as PDFs and slide decks.
A critical feature of the system is its document-level access control, which preserves existing SharePoint permissions. The assistant only returns passages that a specific employee is authorized to view, ensuring data governance is maintained without rebuilding authorization models. Furthermore, the architecture employs hybrid search, combining semantic and keyword-based retrieval to ensure precise results, such as product codes, are captured effectively.
Development and Scalability
The development of both the frontend and the underlying AWS architecture was accelerated by using Kiro, an agentic development environment, running on Anthropic’s Claude Haiku 4.5 model. This allowed a small team to build and iterate on the assistant rapidly. By structuring the system with a dedicated knowledge base per assistant, IskayPet can easily scale to new business areas or countries by pointing the system to the relevant document libraries.
According to the company, the assistant has already driven significant productivity gains, with search times dropping from 3–5 minutes to approximately 7 seconds. As the company continues to refine the system, it plans to integrate Amazon Bedrock Guardrails to enforce responsible AI policies and evaluate new foundation models as they become available on the platform.
Source: aws.amazon.com
Original article: https://aws.amazon.com/blogs/machine-learning/how-iskaypet-unified-corporate-knowledge-with-amazon-bedrock-managed-knowledge-bases/
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