Advanced vector search in public sector: How to generate contextual answers in air-gapped environments

Public sector organizations are at a turning point in adopting generative AI (GenAI). They're under increasing pressure to scale pilots into production-ready experiences — while keeping sensitive data secure. Meeting this demand is complicated by unlimited AI options, limited developer resources, massive volumes of structured and unstructured data, and the realities of air-gapped environments where internet access isn't possible. The key to overcoming these challenges: search.

Retrieval augmented generation (RAG) has already reshaped how we think about AI. Pairing RAG with advanced vector search unlocks the next leap forward: agentic AI interfaces that interpret questions, break them into tasks, and return fact-based contextual answers instead of links.

In this webinar, we'll demo innovations from Elastic, including:

• Support for multiple LLMs and cloud environments, as well as offline and air-gapped deployments
• Inference and retriever APIs that register and manage AI services seamlessly across environments
• Out-of-the-box vector models E5 (dense, multilingual) and ELSER (sparse, open source), with flexibility to bring your own models
• Advanced multi-modal search across dense PDFs, video, and image files
• Propriety vector compression for up to 32x RAM savings without sacrificing recall quality

Additional resources

• How to deploy Elastic Agents in air-gapped environments
• Understanding data mesh in public sector: Pillars, architecture, and examples
• AI and ML for mission systems: How AWS, Anthropic, and Elastic can drive resilience for national security