How Vectorize.io Uses Elastic to Deploy Agentic AI in Hours

Vectorize.io was founded to solve one of the most persistent blockers in enterprise AI adoption: getting large language models to reliably find the right information within massive, heterogeneous document sets. Industries like law, insurance, and finance deal with thousands of nearly identical documents—contracts, policy documents, SEC filings—where a query for “Goldman Sachs on Adobe’s Q3 2024 earnings call” must return exactly that result, not a similar-sounding Q2 or Q4 filing. Standard similarity search wasn’t precise enough, and building custom retrieval infrastructure from scratch was slow and expensive.

Before Elastic, the typical approach for teams building RAG architectures was to stitch together multiple components: a vector store, a keyword search engine, an embedding pipeline, and a query orchestration layer. This meant weeks of engineering work just to validate an approach, before any domain-specific tuning began. For fast-moving clients who needed AI agents to be operational quickly, that timeline was prohibitive.

Vectorize built its data pipelines to connect directly to Elasticsearch, using its hybrid search capabilities—combining semantic vector search with sparse vector and BM25 keyword search—to power retrieval for AI agents. The company runs Elastic Cloud Serverless on AWS with Amazon Bedrock as the LLM and embedding model provider, enabling a seamless experience for clients already in the AWS ecosystem. Elasticsearch Query Language (ES|QL) became a critical tool for high-precision retrieval across large volumes of similar documents, ensuring AI agents consistently return the right result rather than a merely relevant one. When Elastic released its serverless offering, Vectorize migrated from dedicated clusters, gaining built-in multi-tenancy and the ability to spin up indices per customer without operational overhead.

The most striking result: Vectorize can deliver a fully operational AI solution for a new client in about two hours. The same capability built in-house—including search index construction, field extraction, and testing—took at least two weeks. One client whose developer community grew by a million users in a single year deployed a real-time support agent on their Discord platform through Vectorize. When the AI agent couldn’t answer a query, a human stepped in—and Vectorize immediately captured that answer, indexed it in Elasticsearch, and made it available for all future queries with no reprocessing delay. The system learned continuously from human fallback responses.

Vectorize’s integration with Elastic positions it as a platform that scales from day-one simplicity to full agentic AI sophistication. Clients can start with basic document search and progressively layer in hybrid retrieval, vector search, and agentic workflows as their use cases mature. The company is developing an AI Researcher agent that acts as an AI employee—allowing different parts of an organization to ask role-specific questions and surface business signals that frontline support might otherwise miss. This trajectory reflects a broader shift: enterprises are moving from AI experiments toward AI infrastructure that learns and improves in production.