Generative AI with Qlik Answers

What is Qlik Answers?

Qlik Answers is a plug-and-play, generative AI-powered knowledge assistant that provides business users with personalized, contextually relevant answers to questions sourced from unstructured content. Unlike traditional search, generative AI delivers personalized answers to questions instead of just lists of content.

Responses are derived from a number of underlying sources and documents that have been carefully curated into domain-specific knowledge bases. You just ask it a question and get an answer – it’s that simple. Answers are reliable and consistent, and with full explainability, you’ll always know where things came from and have access to those sources – ensuring consistency, trust and transparency.

Why Qlik Answers?

The opportunity

Qlik has long been a leader in structured analytics and data integration. However not all data in organizations is structured data. According to Forester, 80% of the worlds data is unstructured and 70% of enterprises agree they are not effectively leveraging the value in this data. Search engines have been seen as a way to leverage this data, however they provide limited benefits in this space as they are really only doing pattern matches and often return irrelevant results.

Gen-AI (generative artificial intelligence) has grown massively over the last year as a solution for unstructured data. Gen-AI's ability to answer natural language questions and create content based on existing knowledge provides solutions such as virtual assistants, code generation, and process optimization. When used in an Enterprise context, Gen-AI helps provide value from that 80% of unstructured data.

The challenges

However, the barrier to entry for Gen-AI is considerable. While LLMs (large language models) receive a lot of attention, the reality is this is just one of many technologies needed to implement a Gen-AI solution. The cost and complexity to do this well is is beyond the resources of all but the largest organizations. This necessitates most organizations using a third party solution. In most cases this involves storing the organization's data with that third party and risks questions being answered not just with an organization's own data, but with other publicly available data which may be outdated or inaccurate. Also, many of these solutions are subject to hallucinations, where content is made up by the Gen-AI solution. These solutions typically lack the governance and security that organizations come to expect around their data and raise the risk of legal issues, reputational issues, or both.

The solution

These factors are why we have developed our Gen-AI solution, Qlik Answers. Qlik Answers is an Enterprise-grade, plug-and-play, generative AI-powered knowledge assistant that provides business users with personalized, contextually relevant answers to questions sourced from unstructured content. Running on the Qlik Cloud platform, Qlik Answers leverages Qlik Cloud's existing security and governance features to provide an Enterprise-ready, plug-and-play, generative AI solution.

See the Security and Governance section of our Qlik Cloud platform guide for more information.

A mature foundation in AI

Qlik's AI solutions are not new - we have been augmenting our analytics solutions with AI technologies for more than 6 years. In January 2024, Qlik acquired Kyndi, an innovator in natural language processing, search, and generative AI. Kyndi's proprietary technology along with an experienced team of generative AI experts have enabled Qlik to quickly integrate Gen-AI into our existing AI teams and bring a solution to market which provides a plug-and-play solution to our customers.

How Qlik Answers works

Qlik is building a simple, plug-and-play solution that can be deployed out-of-the-box without the complexity of custom-built solutions. However, simple does not mean simplistic – Qlik Answers uses cutting edge semantic search, vector databases, generative AI, and RAG (retrieval augmented generation) technologies under the hood.

Components of Qlik Answers

Qlik answers consists of the following components:

• Enterprise connectivity - Using our existing file store connectors, we can access your organization's data in-place.

• Managed knowledge bases - These contain references to your organization's data. Knowledge bases are generally based around a particular subject. For example, a customer may have HR, Finance, and Sales knowledge bases. Knowledge bases do not store all your source data in Qlik Cloud (unless you are using a space's DataFiles storage as your source). They contain an index of that data in a numeric format.

• Embeddable assistants - These are the part of Qlik Answers that users interact with. An assistant can be based on one or more knowledge bases. This allows us to provide customized assistants to different audiences without having to duplicate content. Assistants are available though Qlik Cloud or can be embedded into your organization's own web portals. Users can provide feedback on answers which is available for review through the Answer Review Portal.

• Explainable answers - Answers returned from assistants provide references to the documents the answers were sourced from. This allows customers to understand how the answer was derived.

• Answer review portal - This allows administrators to see what questions users are asking and any feedback provided. This information can be used to identify areas of interest and potential areas of improvement if users are not satisfied with the answers given.

Technology behind Qlik answers

Behind the major components of Qlik answers are over 25 different Answers-specific processes and numerous other processes of the Qlik Cloud platform. A few of the key technologies used within Qlik Answers are:

• Large Language Models (LLMs) - A large language model (LLM) is a computational model notable for its ability to achieve general-purpose language generation and other natural language processing tasks such as classification. LLMs are very large deep learning models that are pre-trained on vast amounts of data. See What are Large Language Models? for more information.

• Semantic search - Semantic search seeks to improve search accuracy by understanding the searcher's intent and the contextual meaning of terms to generate more relevant results. See Semantic search for more information.

• Retrieval Augmented Generation (RAG) – RAG is a technique by which you include context retrieved from some form of search to generate a result. See What is Retrieval-Augmented Generation? for more information.

How Qlik Answers uses these technologies

The knowledge bases connect to your data sources and index the source documents for semantic search. When a user asks a question, the assistant performs a semantic search against the knowledge base and then uses RAG to pass that data to the LLM, which then provides an answer. The answer includes references back to the relevant documents in your knowledge base. The process flow is as follows:

1. User asks a question to the assistant.
2. Embedding - The question is embedded using the same model used during ingestion.
3. Index and search - The system will find the top 5 chunks that are semantically relevant.
4. Prompt injection and answer generation - A system prompt, the original question, and the relevant chunks are injected into a LLM to generate the answer.

Third-party technologies

The following third-party technologies are used as part of Qlik answers at the time of writing. Qlik may choose to introduce additional or alternate technologies for Qlik Answers in the future.

• Temporal - Temporal is used to distribute workflows in the file ingestion workflow (i.e. Knowledge Base indexing).

• AWS Bedrock - AWS Bedrock is used as a service to connect to Claude.

• OpenSearch - OpenSearch is the vector store that stores embeddings extracted from documents. OpenSearch is also the semantic search engine that is used to gather relevant text chunks during answer generation.

• Cohere Rerank - Cohere Rerank is used to rank the most relevant embeddings returned by OpenSearch.

• Anthropic Claude - Claude (currently v3) is the generative AI large language model we use for Qlik Answers.

• Amazon SageMaker - Amazon SageMaker is used to host the Cohere Rerank model.

Access control in Qlik Answers

Qlik Answers leverages the existing access control model in Qlik Cloud. Access is restricted both by space access and by roles. From a user's point of view, it is possible to provide access to the assistant and only some of the knowledge bases connected to the assistant, so certain privileged data can be restricted to only certain users. It is also possible to restrict access to the source data, so users may be able to receive an answer, but not see the document the answer was derived from.

From an administrative point of view, the same approach is used. It is possible to allow users to create an assistant against a knowledge base, without having access to change or create knowledge bases. It is possible to allow the indexing of existing knowledge bases without allowing the creation of new knowledge bases.

These controls can be assigned to users or groups and are fully governed and audit logs are available either through the platform UI or via API integration into your external systems.

Data security in Qlik Answers

Data stored in Qlik Answers knowledge bases is secured the same way as other data using your tenant's unique encryption keys. Data never leaves the tenant's region except if presented back to the user in response to a question, which leverages our platform's existing secure connections. See the Data separation, storage, and transport section of our "Standards and compliance" page for more information.

The LLM used for Qlik Answers is hosted within Qlik's Cloud storage infrastructure in the same region as your tenant. Qlik Cloud connects to the LLM via a private network link and is not accessible externally. The LLM does not have full nor permanent access to your knowledge bases. It is only the semantic search results used by RAG that are made available to the LLM. No data is retained by AWS or AI model providers.