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Empowers HR teams to deliver fast, accurate, and personalized employee support through conversational AI, automated knowledge management, and seamless HR system integration.
Specialized Knowledge Base for Insurance Information Sets
Turns standardized insurance Information Sets into a searchable knowledge base to support compliance and digital operations.
#Insurance #KnowledgeBase #DocumentUnderstanding
Business Challenge
In the insurance industry, companies are required to provide an Information Set before clients sign any policy. These documents, both for life and non-life insurance, are often long, complex, and difficult to navigate. To ensure transparency, comparability, and consumer protection, their structure follows standardized formats regulated at the European level by EIOPA (European Insurance and Occupational Pensions Authority).
Although Information Sets share a common template—such as DIP (pre-contractual documents that describe insurance products), DA (declarations that assess the suitability of a product for the customer), and KID (standardized documents summarizing key features and risks of investment-based products)—they are stored in unstructured formats, making it hard to search or retrieve specific information, use them as reliable input for AI or digital assistants, or reuse knowledge efficiently across systems and departments. Manual curation of this content is costly and error-prone, and limits the ability to reuse information efficiently for internal tools, customer portals, or AI applications.
Solution Overview
Our solution enables the automated construction of a specialized knowledge base starting from Information Sets, transforming information set documentation into machine-readable, enriched, and interlinked data.
A Knowledge Base is built as a graph, where each node represents an Information Set section and its related content. The graph structure allows not only to interlink related concepts and clauses across documents, but also to organize information directly within the nodes. Existing documentation is parsed and structured using the shared Information Sets format, enriched with AI-generated metadata such as topics, summaries, and highlights, and embedded into a vector space for semantic search.
The solution's main features include:
AI-Powered Parsing and Metadata Extraction
Documents are automatically processed using GenAI models to identify sections (DIP, DA, KID, etc.), extract semantic content, and generate metadata such as topic tags, summaries, clause classification, and key-value highlights like exclusions, guarantees, and limits.Document Structuring Based on Shared Format
Thanks to the consistent layout of Information Sets, the system applies deterministic and machine learning-based parsing techniques to reliably extract information from each section.Knowledge Graph Construction
A graph layer is created to connect related content across documents, enabling the tracking of shared clauses, visual exploration of guarantees and exclusions, and document-to-document linking based on contextual similarity.Conversational Retrieval Interface
The knowledge base can be queried using natural language, allowing users to retrieve insights and specific clauses with questions.Multi-Use Knowledge Base
This structured Knowledge Base supports multiple use cases, including intelligent document search tools, product advisory platforms, agent support solutions for verifying customer coverage, and any AI application requiring accurate access to domain-specific insurance content.
Technical Implementation
This Generative AI solution was built with:
Document Parsing & Structuring
The parsing engine identifies the structured components of the Information Sets by analyzing layout, formatting, and section headers. It supports both natively digital and scanned documents through OCR and document image processing.Metadata Enrichment
Generative AI models like GPT-4o are used to automatically generate summaries, topic classification, and clause detection, enhancing the structure and findability of the content.Embedding & Vector Storage
Content is embedded using domain-specific language models and stored in a vector database, such as Azure AI Search, enabling high-performance semantic retrieval and RAG pipelines.Integration Layer
All components are exposed through REST APIs and connected to enterprise systems using LangChain tools, enabling seamless integration with digital platforms, assistants, and back-office services.
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