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Artificial Intelligence 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. 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. 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 selectively. The AWS Regional deployment model and capacity reservation options give Qlik headroom to keep scaling today. When ready, services like Amazon Bedrock AgentCore give Qlik the option to offload more of the operational overhead for specific workloads, without requiring a full migration to get there. Qlik uses Bedrock through its own LLM gateway rather than binding application logic directly to one model. Product teams can change model choice by task without rewriting the surrounding application. That separation matters for a system that expects model strategy to keep evolving. Planning capacity ahead of demand Because Qlik forecasts model capacity 3–6 months ahead of major launches, the team built a working model for projecting token consumption by feature and Region, then checked actual usage against those projections after each rollout. That discipline is part of why the February general availability scaled without straining under its own adoption. Business outcomes and customer impact Since the February 2026 general availability of Qlik’s agentic experience, Discovery Agent has surfaced more than 100,000 discoveries for customers, and the majority of Qlik Cloud accounts with agentic tools turned on are actively using their agents rather than trying them once and stopping. Customer-level results reported separately include Lintech International’s 75% faster response times and up to 7 hours per week returned to business managers, and Bystronic’s 15-minute chatbot deployment. Lessons learned Building and scaling Qlik Answers across 40,000+ customers surfaced a few lessons that might help other teams building agentic tools on Bedrock: - Separate fast routing from deep reasoning early. Bundling them means every request pays for more processing than it needs. - Standardize on one orchestration framework for multi-agent execution rather than letting each new specialist invent its own runtime. It’s easier to operate one application than a set of disconnected agent systems. - Treat safety and grounding checks as a system-level gate, not a per-feature afterthought. Running them once, at the model access layer, means every new specialist agent inherits the same protections automatically. - Use feature flags at the tenant level to expand capability gradually. This keeps new, less-proven capabilities from affecting the reliability of the core experience. - Build a capacity forecasting habit before you need one. Validating projected usage against actual usage after each launch is what turns capacity planning from guesswork into a repeatable process. What’s next Qlik’s roadmap keeps the architecture deliberately open and adaptable. The team is evaluating Amazon Bedrock AgentCore for select workloads where a managed agentic runtime would reduce operational overhead, while continuing to build on open standards and its own orchestration layer where that gives Qlik more control over cost, latency, or portability. The goal is to keep the option to adopt managed services where they help, without designing the framework around any one of them. Qlik is also building a systematic framework for evaluating model performance. This means Qlik can move to better-fit models as they become available, without disrupting the experience customers already rely on. Conclusion Qlik Answers’ adoption since its February 2026 launch reflects an architecture built to stay out of the way: fast where speed matters, deliberate where accuracy matters, grounded in real sources throughout, and safe enough for regulated industries to depend on. The model flexibility, Regional sovereignty controls, and Guardrails-based content filtering and grounding validation in Amazon Bedrock gave Qlik a foundation to build that experience at global scale. The result so far includes more than 100,000 discoveries surfaced, hours returned to employees at companies like Lintech and TouchPoint, and a majority of accounts with agentic tools turned on continuing to use their agents after trying them. To see what Qlik Answers can do with your data, start a free trial of Qlik Cloud Analytics, request a Qlik Answers demo or a Qlik and AWS demo, or visit the Qlik Answers product page to learn more.
document fetched 2026-10-08 02:55 · canonical https://aws.amazon.com/blogs/machine-learning/how-qlik-built-grounded-enterprise-scale-ai-with-amazon-bedrock