Summary of Elastic (NYSE:ESTC) Analyst Day - October 09, 2025 Company Overview - **Company**: Elastic (NYSE:ESTC) - **Event**: Financial Analyst Day - **Date**: October 09, 2025 - **Key Speaker**: Ash Kulkarni (CEO), Eric Prengel (Global VP of Elastic and Head of Investor Relations) Core Industry and Company Insights - **Industry**: Data management and analytics, focusing on unstructured data - **Company's Role**: Elastic is recognized as the world's most popular data platform for unstructured data, with over 5.5 billion downloads of its software, averaging over three downloads per second over 15 years [6][7][8] - **Competitive Advantage**: Elastic's ability to handle unstructured data is its greatest competitive advantage, with over 30 petabytes of new data ingested daily into paid clusters globally [7][9] Key Points and Arguments 1. **Unstructured Data Growth**: The company emphasizes the increasing importance of unstructured data, particularly in the context of AI and large language models (LLMs) [9][10] 2. **AI Integration**: Elastic's platform is positioned as a natural choice for AI applications due to its capabilities in managing unstructured data, which is crucial for training AI models [11][12] 3. **Product Announcements**: Six new product capabilities were announced, including: - **Agent Builder**: A tool for building AI agents directly on top of data [17] - **Elastic Inference Service**: A GPU-accelerated service for embedding and retrieval models [17] - **Acquisition of Jina AI**: Enhances Elastic's capabilities in multilingual and multimodal models [18] 4. **Customer Use Cases**: Notable customers include: - **DocuSign**: Chose Elastic for its intelligent agreement management platform, needing to search billions of documents [20] - **Legora**: An AI-native company that utilizes Elastic for legal research and drafting [21] - **National Health Service (NHS)**: Uses Elastic for patient record management, emphasizing data privacy and relevance [21] 5. **Observability and Security**: Elastic's observability platform is built to handle messy data, with over 90% of Elastic Cloud Observability customers using it for log analytics [28][30] 6. **Market Position**: Elastic is recognized as a leader in its field by analysts, with over 50% of Fortune 500 companies as customers, indicating significant growth potential [37] Additional Important Insights - **Context Engineering**: The concept of context engineering is highlighted as vital for AI applications, ensuring that LLMs have the right data and context to function effectively [55] - **Developer Community**: Elastic has a strong developer community, with 17% of professional developers and 19% of AI developers using Elasticsearch, showcasing its popularity and trust [56][57] - **Performance Improvements**: Recent enhancements include a new data lake architecture that maintains high performance while providing scalability and efficiency [47] Conclusion - **Future Outlook**: Elastic is well-positioned to capitalize on the growing demand for unstructured data management and AI integration, with a strong product lineup and a diverse customer base [39][38]

Graph RAG Overview - Graph RAG aims to enhance LLMs by incorporating knowledge graphs, addressing limitations like lack of domain knowledge, unverifiable answers, hallucinations, and biases [1][3][4][5][9][10] - Graph RAG leverages knowledge graphs (collections of nodes, relationships, and properties) to provide more relevant, contextual, and explainable results compared to basic RAG systems using vector databases [8][9][10][12][13][14] - Microsoft research indicates Graph RAG can achieve better results with lower token costs, supported by studies showing improvements in capabilities and analyst trends [15][16] Knowledge Graph Construction - Knowledge graph construction involves structuring unstructured information, extracting entities and relationships, and enriching the graph with algorithms [19][20][21][22] - Lexical graphs represent documents and elements (chunks, sections, paragraphs) with relationships based on document structure, temporal sequence, and similarity [25][26] - Entity extraction utilizes LLMs with graph schemas to identify entities and relationships from text, potentially integrating with existing knowledge graphs or structured data like CRM systems [27][28][29][30] - Graph algorithms (clustering, link prediction, page rank) enrich the knowledge graph, enabling cross-document topic identification and summarization [20][30][34] Graph RAG Retrieval and Applications - Graph RAG retrieval involves initial index search (vector, full text, hybrid) followed by traversing relationships to fetch additional context, considering user context for tailored results [32][33] - Modern LLMs are increasingly trained on graph processing, allowing them to effectively utilize node-relationship-node patterns provided as context [34] - Tools and libraries are available for knowledge graph construction from various sources (PDFs, YouTube transcripts, web articles), with open-source options for implementation [35][36][39][43][45] - Agentic approaches in Graph RAG break down user questions into tasks, using domain-specific retrievers and tools in sequence or loops to generate comprehensive answers and visualizations [42][44] - Industry leaders are adopting Graph RAG for production applications, such as LinkedIn's customer support, which saw a 286% reduction in median per-issue resolution time [17][18]

Problem Statement & Solution - Current memory frameworks struggle with relevance, leading to inaccurate responses or hallucinations due to the storage of arbitrary facts [3][4][5] - Semantic similarity does not equate to business relevance, as vector databases lack causal or relational understanding [7] - The industry needs domain-aware memory solutions instead of relying solely on better semantic search [8] - Zep offers a solution by enabling developers to model memory after their specific business domain, creating more cogent and capable memory [1][2] Zep's Implementation & Features - Zep allows developers to define custom entities and edges within its graph framework, tailoring memory to specific business objects [1][9] - Developers can use Pydantic, Zod, or Go structs to define business rules for these entities and their fields [9][10] - Zep's SDK allows defining entity types with descriptions and business rules for fields, enabling precise control over data stored [10] - Zep allows building tools for agents to retrieve financial snapshots by running multiple searches concurrently and filtering by specific node types [10][11] - Zep's front end provides a knowledge graph visualization, allowing users to see the relationships and fields defined for each entity [12] Demonstration & Use Case - A finance coach application demonstrates Zep's ability to store explicit business objects like financial goals, debts, and income sources [8][9] - The application captures relevant information, such as a $5,000 monthly rent, and stores it as a debt account entity with defined fields [11][12]

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