Elastic Expands Collaboration with Siren to Enhance AI-Powered National Security Investigations
Strategic Investment: Elastic NV has invested in Siren to enhance their partnership and accelerate the development of Siren's AI-driven investigative platform, integrating Elastic's technology with Siren's tools for national security and law enforcement.
Response to Threats: The collaboration aims to address various threats such as cybercrime and terrorism, providing faster coordination and clearer investigative workflows compared to proprietary systems.
Financial Performance: Elastic reported strong fiscal second-quarter results with adjusted earnings of 64 cents per share and revenue of $423.48 million, exceeding analysts' expectations and showing significant growth in Elastic Cloud revenue.
Future Projections: Elastic has raised its earnings and revenue forecasts for the upcoming quarters, projecting adjusted earnings of 63 to 65 cents per share and revenue between $437 million to $439 million for the third quarter.
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- Earnings Release Schedule: Elastic will announce its financial results for the third quarter of fiscal 2026 on February 26, 2026, after the U.S. market closes, providing insights into the company's latest financial performance.
- Conference Call Timing: The company will host a conference call at 2:00 p.m. PT (5:00 p.m. ET) on the same day to review its financial results and business outlook, aiming to bolster investor confidence in the company's future growth.
- Webcast Availability: The conference call will be accessible via a live webcast on Elastic's investor relations website, allowing real-time information access, with a replay available for two months to ensure that investors who cannot attend live can still obtain critical insights.
- Company Background: Elastic, known for its expertise in search technology and artificial intelligence, has its Search AI Platform utilized by over 50% of Fortune 500 companies, highlighting its significant industry impact and market recognition.

- Service Launch: Elastic's Inference Service (EIS) is now available via Cloud Connect for self-managed Elasticsearch deployments, allowing users to access on-demand capabilities without managing GPU infrastructure, significantly lowering the technical barrier for adoption.
- Model Access: Users can now immediately access multilingual and multimodal embedding models provided by Jina.ai, enhancing the quality and relevance of search results, which in turn improves customer experience and business decision-making capabilities.
- Infrastructure Simplification: EIS enables self-managed clusters to securely offload embedding generation and search inference to Elastic Cloud's managed GPU fleet while maintaining their existing architecture and data, optimizing resource allocation and operational efficiency.
- Rapid Deployment Advantage: With a single setup, self-managed customers can quickly access a range of cloud services from automated diagnostics to fast AI inference, further driving the application of data-driven decision-making in enterprises and enhancing market competitiveness.
- Low-Latency Inference: Elastic's newly launched Jina Reranker models on the Elastic Inference Service deliver low-latency, multilingual processing, significantly enhancing search quality by helping users quickly find the most relevant matches across multi-query results, thereby improving user experience.
- Production-Friendly Architecture: Jina Reranker v3 is optimized for low-latency inference, capable of reranking up to 64 documents in a single inference call, which reduces inference usage and is well-suited for RAG and agentic workflows requiring defined top-k results, thus enhancing overall efficiency.
- Unbounded Candidate Support: Jina Reranker v2 scores documents independently, allowing it to handle arbitrarily large candidate sets, enabling developers to incrementally rerank results without relying on strict top-k limits, thereby increasing flexibility and accuracy.
- Expanded Model Catalog: The release of these models extends Elastic's existing catalog on EIS, integrating open-source multilingual and multimodal embeddings, further strengthening Elastic's competitive position in the search and AI sectors, with more models expected to be added to meet market demands.
- Inference Service Launch: Elastic announced the availability of Elastic Inference Service (EIS) via Cloud Connect for self-managed Elasticsearch deployments, enabling organizations to access cloud-hosted inference capabilities on-demand while avoiding GPU infrastructure management, thereby enhancing operational efficiency.
- Seamless Integration: Launched in Elasticsearch 9.3, EIS allows users to leverage GPU-based embedding and reranking models, including leading models from Jina.ai, enabling rapid implementation of powerful semantic search capabilities that improve the quality of search results.
- Data Security: Self-managed clusters can retain their existing architecture and keep data on-premises while securely offloading embedding generation and search inference to Elastic Cloud's managed GPU fleet, ensuring data privacy and security.
- Operational Simplification: According to Steve Kearns, Elastic's General Manager of Search, EIS simplifies the complexity of GPU infrastructure, making it easier for self-managed customers to adopt semantic search, offering a range of cloud services from automated diagnostics to fast AI inference, thus enhancing customer experience.
- Comprehensive Agent Builder: Elastic's newly launched Agent Builder provides developers with a complete set of capabilities to quickly build secure, reliable, context-driven AI agents, significantly enhancing enterprise data search and analysis capabilities.
- Seamless Microsoft Integration: Agent Builder's native MCP and A2A protocol support enables seamless deployments within Microsoft Foundry and Microsoft Agent Framework, enhancing users' ability to build context-rich AI agents.
- Workflow Functionality Extension: Elastic also introduced Elastic Workflows (tech preview), allowing agents built with Agent Builder to reliably take action across systems, closing the reliability gap in AI automation.
- Model Agnosticism and Compatibility: Agents developed with Agent Builder are model-agnostic and compatible with managed model-as-a-service providers, including cloud hyperscalers, ensuring broad applicability and flexibility.
- AI Agent Development: Elastic's newly launched Agent Builder provides developers with a comprehensive set of capabilities to quickly build secure, reliable, context-driven AI agents, significantly enhancing the efficiency and accuracy of enterprise data processing.
- Context-Driven Intelligence: Built on Elasticsearch, Agent Builder effectively extracts enterprise context from unstructured data sources, enabling teams to reason more accurately and deliver better outcomes, thereby improving the quality of business decisions.
- Workflow Functionality: Elastic also introduced Elastic Workflows (tech preview), allowing agents built with Agent Builder to reliably execute actions across systems, addressing the reliability gap in AI automation and facilitating the transition from pilot projects to real-world applications.
- Compatibility and Availability: Agent Builder is now available in Elastic Cloud Serverless and included in the Enterprise Tier of Elastic Cloud Hosted, ensuring existing customers can seamlessly access this new functionality to enhance their AI agent development capabilities.





