How Qlik built grounded, enterprise-scale AI with Amazon Bedrock

Building grounded AI on Amazon Bedrock, Qlik Answers tackles a common enterprise problem: employees aren’t short on data. They are short on a fast way to ask a real question of it and get an answer they can trust. Analysts spend hours searching through documents, dashboards, and institutional memory to answer things a colleague could ask in a sentence. And when organizations try to close that gap with generative AI, many run into the same wall: answers without sources, no way to verify a result, and no path to deploy the tool in a regulated environment. Qlik , a global leader in data integration, data quality, analytics, and AI, built Qlik Answers to solve that problem for its more than 40,000 customers worldwide. Since general availability in February 2026, Qlik Answers has moved from an early capability to daily use: Qlik’s Discovery Agent , an AI-driven anomaly and outlier detection agent, alone has surfaced more than 100,000 discoveries for customers since launch, and the majority of Qlik Cloud accounts with agentic tools turned on are actively using them. This post explains how Qlik built the tool behind that adoption, and how Amazon Bedrock supports it at global scale. Challenge Qlik identified three problems it needed to solve at the same time to bring Qlik Answers to production across its customer base: Orchestrating specialized reasoning without slowing anyone down. Enterprise questions vary widely: some need a quick lookup, others require pulling from structured analytics, unstructured documents, glossary definitions, or automations. A single, general-purpose assistant tends to get slower and less accurate as more capability gets added to it. Meeting data sovereignty requirements across Regions. Qlik serves customers across Europe, Asia Pacific, and the Americas, each with its own data residency expectations. A single global deployment model wouldn’t satisfy those requirements, but 11 separate Regional builds would be difficult to maintain and improve consistently. Forecasting model capacity months ahead of demand. Token consumption and model availability needed to be planned 3–6 months before major launches. Qlik then validated these projections against actual usage once customers started using the product, so growth in adoption doesn’t turn into a capacity problem. Solution overview Qlik Answers gives employees, not only analysts, a single place to ask a natural-language question and receive a grounded, sourced answer. Depending on the question, that answer can come from a knowledge base, a live analytics app, a glossary definition, or a document. The underlying system decides which path to take. Three deployments illustrate what that looks like for customers: Bystronic , a global sheet-metal processing specialist, deployed an AI chatbot in 15 minutes, giving employees a way to query real-time operations data and get sourced answers across departments. Lintech International indexed more than 17,000 technical documents in Qlik Answers, cutting manual research time and speeding up response times by 75 percent, which gave business managers back as much as 7 hours a week. TouchPoint Support Services , which serves 650 healthcare sites and is aiming to double in size, uses Qlik Answers to give fast, compliance-aligned guidance to 15,000 staff working in regulated, fast-paced environments. To support this at scale, Qlik built the system around clear architectural boundaries rather than one large assistant. The key layers are as follows: Entry layer . A stable conversational entry point inside Qlik Cloud, so new backend capability can be added without changing how customers interact with the assistant. Routing layer . A lightweight layer that reads the user’s message and conversation context and decides where the request should go. Its only job is a fast, accurate routing decision, not solving the task itself. Answer layer . Coordinates response generation once a request is routed, choosing between a fast path for simple requests and a more deliberate path that breaks a question into sub-questions and pulls from multiple sources. Specialist agent layer . A shared swarm runtime defines how specialist agents, tools, state, and human-in-the-loop steps work, so new specialists can be added without each one inventing its own orchestration model. Conversational analytics layer . Handles structured data questions by routing them to an app-aware reasoning path built for analytics rather than general text generation. Retrieval layer . Unstructured document indexing and retrieval runs on Amazon OpenSearch Service, which stores and searches the content that grounds Qlik Answers’ responses to knowledge-base and document questions. Model access layer . Reached through Qlik’s own large language model (LLM) gateway, this layer connects to Amazon Bedrock for chat, streaming, embeddings, and reranking. It also applies Amazon Bedrock Guardrails to every request and response: content filtering for prompt injection, personally identifiable information (PII), secrets, and denied topics, plus a grounding-validation check on generated answers. When a model a customer relies on is not yet available in-Region through Bedrock, Qlik hosts it on Amazon SageMaker AI instead, then moves that workload back to Bedrock after Regional availability catches up. For model availability by AWS Region, refer to Supported models by AWS Region in Amazon Bedrock . Figure 1: Employee questions flow through the entry, routing, and answer layers, then branch to specialist agents, the analytics agent, and the retrieval agent (backed by Amazon OpenSearch Service), before converging on the model access layer, which reaches foundation models available on Amazon Bedrock, with Amazon SageMaker AI as an in-Region fallback Grounding answers in real sources Grounding was one of the more important design decisions in the framework. The answer layer pulls from several kinds of context depending on the question: structured app metadata, retrieved knowledge base content, glossary definitions, automation context, and document content for summarization. After the elements of a complete answer are returned, the full response is assembled and presented to the user. Retrieval alone doesn’t guarantee accuracy. Qlik also runs a grounding-validation check on generated answers, comparing each one against the source content it drew from, using the contextual grounding capability in Amazon Bedrock Guardrails. That turns grounding from a retrieval step into a checked production control. For unstructured questions, the tool retrieves question-specific content from Amazon OpenSearch Service and carries that context forward so the final answer includes citations. For structured questions, it hands the work to the conversational analytics path. Further capabilities, including glossary lookup, automations, and document summarization, roll out with capability controls, so Qlik can expand what the tool can do without destabilizing the default experience for every customer at once. Tenant-specific capability options mean Qlik can rebuild the available orchestration path per request, so capabilities can be turned on selectively while the overall architecture stays consistent across customers. Figure 2: The specialist agent swarm, where shared state, tools, and human-in-the-loop steps let new specialist agents plug into a common runtime Why Amazon Bedrock Qlik evaluated its model strategy against the three problems described earlier, and Amazon Bedrock addressed each one directly. Multi-model flexibility. With Bedrock, Qlik can assign the best-suited model to each agent’s task instead of locking the whole system to one model’s tradeoffs. Data sovereignty at scale. With Amazon Bedrock cross-Region inference ( CRIS ), Qlik can serve all 11 Regions while keeping data resident where compliance requires it. This combines global reach and sovereignty controls in a single managed capability. Room to adopt managed services selecti