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IskayPet’s office teams had a knowledge challenge. Answers to everyday operational questions, such as a return policy, a veterinary protocol, or an onboarding step, lived in hundreds of Microsoft SharePoint documents spread across four languages and three countries. Finding the right information in large document repositories took a long time. Every new store, country, or procedure made the problem bigger. Retrieval Augmented Generation (RAG) is the usual way to solve this. But running RAG in production normally means operating vector databases, embedding pipelines, and ingestion jobs yourself. In this post, we show how IskayPet, a leading pet care company in Iberia, built a corporate knowledge assistant on Amazon Bedrock Managed Knowledge Bases. The assistant answers employee questions across retail operations, veterinary care, people and onboarding, and procurement, in four languages and across three countries, with no RAG infrastructure to manage. The team built the whole solution, from the store-facing frontend to the AWS architecture and deployment, using Kiro with Claude Haiku 4.5. We cover the architecture, the SharePoint integration, the retrieval configuration decisions, and the results in production. About IskayPet IskayPet is a leading pet care company in Iberia. It operates more than 330 stores across Spain, Portugal, and France under the Kiwoko, Tiendanimal, Kivet, Clinicanimal and Animalis brands. With more than 4,500 employees and 7 million customers, IskayPet runs operations that span retail, veterinary clinics, animal care, procurement, and people and onboarding. Each area has its own policies and procedures, in Spanish, Portuguese, French, and English. The challenge Across every business area, IskayPet office staff searched hundreds of SharePoint documents by hand to answer operational questions about procedures, policies, contracts, and onboarding. With operations in three countries and four languages, this took real time, and the answer was often outdated or wrong. There was no reliable way to query corporate knowledge, and the problem grew with every country, area, or document added. IskayPet needed a solution that could do the following: Answer natural-language questions grounded in their own corporate documents. Work across four languages without maintaining separate systems. Route each question to the right body of knowledge (an area, a country) without cross-contamination. Scale to new areas and countries without re-architecting. Cite its sources so employees can trust and verify answers. Reuse their existing SharePoint repositories without a costly migration. Why Amazon Bedrock Managed Knowledge Bases IskayPet chose Amazon Bedrock Managed Knowledge Bases for its managed RAG infrastructure, which removed the need to run vector stores, embeddings, or ingestion pipelines. The main reasons were: Managed RAG: Amazon Bedrock handles data ingestion, multimodal parsing, vector storage, embedding generation, and retrieval. IskayPet pays only for what it stores and retrieves. Native SharePoint connector: The connector ingests documents directly from IskayPet’s existing SharePoint environment, which avoided a data migration. With advanced indexing enabled, it also processes content from images embedded in documents. Built-in hybrid search: Combining semantic and keyword (BM25) search at no extra cost recovers exact terms, such as product codes and references, that pure semantic search can miss. Managed reranking: Retrieved passages are reordered by relevance at no additional charge. Document-level access control: The knowledge base can enforce the source system’s own permissions at retrieval time, so a query returns only passages from documents the requesting user is already allowed to read. This lets IskayPet reuse SharePoint permissions instead of rebuilding an authorization model. Model flexibility: IskayPet can start with a cost-effective model and change it later without rewriting infrastructure. Native RAG evaluation: The team measured quality at each step with the built-in evaluation tools. Solution overview The following diagram shows the end-to-end flow, from the employee’s question through permission-aware retrieval to the grounded, cited answer. Figure 1: Solution architecture The solution is an assistant that employees reach through their existing channels. When an employee asks a question, the assistant retrieves the most relevant passages from its knowledge base, restricted to the documents the employee is allowed to read. It then generates a grounded, cited answer in the employee’s language. At a high level, the flow works as follows: An employee submits a natural-language question through the chat interface. The application calls the knowledge base for the requested assistant, passing the employee’s identity so that document-level permissions are applied to the retrieval. The knowledge base runs hybrid search (semantic and keyword) over the indexed content, reranks the results, and returns the most relevant passages with their source references. A foundation model (FM) generates a grounded answer from the retrieved passages. The prompt requires source citation and tells the model to say when the answer isn’t in the retrieved content. The employee receives the answer along with the source documents and can leave feedback (thumbs up or down) that feeds the evaluation set. Building the solution with Kiro and Claude Haiku 4.5 IskayPet built the whole solution, from the store-facing frontend to the AWS architecture and service deployment, using Kiro, the agentic development environment, running on Claude Haiku 4.5 on Amazon Bedrock. As new models become available on Amazon Bedrock, IskayPet plans to evaluate and adopt them for each use case. For model availability by AWS Region, see Supported models by AWS Region in Amazon Bedrock. Store-facing frontend – The team used Kiro to build the frontend that store employees use to interact with the assistant, generating and iterating on the UI code with Claude Haiku 4.5. Architecture and deployment – Kiro also helped design the AWS architecture and provision the services behind the solution: knowledge base configuration, data sources, AWS Identity and Access Management (IAM) roles, and the application layer. An agentic workflow with Kiro and a fast, cost-effective model like Claude Haiku 4.5 helped a small team move from concept to production quickly. It also kept the application code and the infrastructure consistent and maintainable. Ingestion: The SharePoint connector with advanced indexing IskayPet’s documents already lived in SharePoint, organized by area and country. Rather than migrate them, the team used the native SharePoint connector in Amazon Bedrock Managed Knowledge Bases. In the data source’s Advanced configurations, under Content indexing and Advanced indexing, IskayPet enabled Visual content in documents. This setting extracts content from images embedded in documents (PDF, DOCX, PPTX) and from standalone images. Figures and scanned content inside procedure documents became searchable without a separate preprocessing pipeline. The connector syncs incrementally, so new and updated documents are picked up on each sync. Multi-country, multi-area architecture A key design decision was how to structure knowledge for four languages, several business areas, and three countries. IskayPet weighed two approaches: A single shared knowledge base, with every document tagged by country and area and a retrieval filter applied per query. This maximizes reuse of shared content but couples all domains into one index. A dedicated knowledge base per assistant, each scoped to the relevant SharePoint sites or libraries. This keeps each body of knowledge isolated end to end and lets each assistant be tuned independently. IskayPet chose a knowledge base per assistant. Each assistant points at specific SharePoint sites and can be narrowed further to specific document libraries or folders through inclusion paths, so it indexes only the intended content and not the whole corporate site. Adding a new area or country means adding an assistant and pointing it at the right content, not re-architecting the system. Access control Access is governed by the users’ existing SharePoint permissions. The assistant doesn’t grant access to anything new: It only makes it more straightforward to query documents an employee could already open. This works on two levels. Which assistants a user sees – An employee is shown only the assistants whose underlying documentation they’re entitled to read, whether that entitlement comes from direct permission, group membership, or access to a specific folder. If access can’t be confirmed, the assistant is hidden rather than shown. Which documents inform an answer – Access is enforced again at retrieval time, per document: The employee’s identity is passed to the knowledge base, which returns passages only from documents that person is authorized to see. Amazon Bedrock Managed Knowledge Bases supports this document-level access control natively, so the same answer to the same question can legitimately differ between two employees with different permissions. Because permissions are read from SharePoint on every access check, revoking or granting access in SharePoint is reflected in the assistant without changes to the application. Retrieval and generation configuration The quality of a RAG system is decided mainly at ingestion, retrieval, and generation. Fine-tuning is a complementary technique. For this use case, IskayPet tuned the following settings: Chunking – Handled by the managed knowledge base, so the team didn’t build or tune a chunking pipeline. This removed a component that’s usually significant operational effort in a self-managed RAG. Hybrid search – The knowledge base combines semantic and keyword retrieval, which recovers exact terms such as product codes that pure semantic search can miss. Number of results – Tuned against an evaluation set. The assistant retrieves 25 passages and, after removing duplicates and fragments too short to add context, injects up to 15 into the prompt. This number is the main cost lever, because it multiplies input tokens on every question. Generation prompt – Enforces a corporate tone, requires source citation, and tells the model to say when the answer isn’t in the retrieved content, which reduces hallucination and adds traceability. As a production control, the team is adding Amazon Bedrock Guardrails to apply content filtering and responsible AI policies to the generated answers. Evaluation– Native Amazon Bedrock RAG evaluation, using the thumbs up and down feedback from real usage as an evaluation set to measure quality before and after each change. Generating the response For answer generation, IskayPet uses a cost-effective foundation model on Amazon Bedrock. Because the knowledge base decouples retrieval from generation, IskayPet can switch or upgrade the generation model without touching the ingestion or retrieval layers. This lets the team balance cost and answer quality as usage grows. Cost efficiency The knowledge base includes multimodal parsing, embedding generation, and reranking at no additional charge, so IskayPet’s ongoing cost comes mainly from index storage, retrieval API calls, and generation-model tokens. In production, input tokens dominate that cost, because of the passages injected into each prompt. The team is evaluating further optimizations as usage grows, including prompt caching for the stable part of the prompt and batch inference for non-interactive workloads. Results In roughly the first two months in production, IskayPet has seen: Faster answers – Time to find an operational answer dropped from 3–5 minutes to about 7 seconds. Higher productivity – Around 95 percent less time spent searching for procedures and policies [truncated for AI cost control]