Benchmarking in preview provides up to 37% better performance on Microsoft Cobalt 100 Arm-based VMs

Elasticsearch delivers performance increase for users running the Elastic Search AI Platform on Arm-based architectures

**Performance Gains**

**Elastic Cloud: AI innovations in the hands of customers**

With Elasticsearch Relevance Engine, you can create AI-powered search apps. Learn how Relativity uses Elastic & Azure Open AI for this goal.

About Relativity

eDiscovery's future with generative AI

Elasticsearch for the future of AI search experiences

Components of an AI-powered search experience

Architectural considerations for an AI-powered search experience

Cost

Scale and flexibility

Privacy and security

Industry-specific considerations: Compliance, air-gapped, private clouds

What will you build with Elastic and generative AI?

The building blocks of an AI-powered search experience are: natural language processing (NLP), vector database, vector search, and model flexibility with large language models integration.

What are the key components of an AI-powered search experience?

Users should consider the following factors when building an AI-search experience: cost, scale & flexibility, privacy and security. Also, keep in mind your industry-specific considerations (compliance, air-gapped, private clouds).

Which considerations should you keep in mind when building an AI-search experience?

Building AI search experiences with Elasticsearch & Azure OpenAI

Relativity uses Elasticsearch and Azure OpenAI to build AI search experiences

Ingest and search complex proprietary documents with Unstructured and Elasticsearch vector database for RAG applications

High level flow

Conclusion

Search complex documents using Unstructured.io and Elasticsearch vector database

Author

Hemant Malik

Articles

Elasticsearch delivers performance increase for users running the Elastic Search AI Platform on Arm-based architectures

Relativity uses Elasticsearch and Azure OpenAI to build AI search experiences

Search complex documents using Unstructured.io and Elasticsearch vector database