How Cypris built an AI research platform for production scale on Elastic

Steve Hafif

CEO

Cypris

Logan Pashby

Principal Software Engineer

Cypris

Serena Chou

Director of Product Management

Elastic

R&D teams operate in a high-stakes environment where speed and accuracy are critical. Cypris, an AI-driven research platform, has transformed how organizations analyze technical and market data — delivering insights in minutes instead of weeks.

Watch this webinar to explore how Cypris built an advanced search and research platform that processes over 500 million data points using the Elasticsearch vector database. Learn how the team harnessed vector search, Better Binary Quantization (BBQ), and retrieval-augmented generation (RAG) to optimize relevance, reduce development costs, and scale for rapid enterprise growth.

If you're working with AI search, vector databases, or retrieval-augmented generation, this session will provide deep technical insights from a real-world, production-scale implementation.

What you'll learn

• Using a native vector database in Elastic: How Cypris saved considerable time and resources using Elastic’s vector database.
• Optimizing AI search at scale: How Cypris delivers relevant results across vast data sets with dense vector search.
• Building a production-ready RAG workflow: Key considerations for integrating generative AI into research applications.
• Technical decisions that drive efficiency: Why Cypris chose Elastic for real-time indexing, query performance, and cost-effective scaling.
• Measurable business impact: How these optimizations accelerated report generation and reduced in-house AI development costs.

Additional resources

• What is vector search?
• The most widely deployed, open source vector database
• [Customer story] Cypris puts Elastic vector search and RAG technologies at the heart of its AI-powered research platform
• [Intro to Cypris] Learn more about Cypris, an AI-powered research platform