How Vectorize.io Uses Elastic to Deploy Agentic AI in Hours
Vectorize.io is a US-based software company that builds agentic and generative AI infrastructure, helping organizations in law, insurance, and finance make vast volumes of unstructured data usable by large language models. By integrating Elastic’s hybrid search and Elastic Cloud Serverless with Amazon Bedrock, Vectorize deploys production-ready AI solutions for clients in hours rather than weeks. One client whose developer community grew by a million users in a year relied on Vectorize’s real-time learning agent—built on Elasticsearch—to answer support queries and instantly index new answers for future use.
Impact
~2 hours
Time to deploy AI solution for new client
1 million new developers in one year
Developer community growth handled by one client
Challenge
Organizations in document-heavy industries like law, insurance, and finance needed AI agents that could retrieve precise information from thousands of near-identical documents, but standard similarity search returned imprecise results and building custom retrieval infrastructure took weeks of engineering effort per client.
Solution
Vectorize integrated its vector data pipelines directly with Elasticsearch, leveraging hybrid search (semantic + BM25 + sparse vector), ES|QL for high-precision document retrieval, and Elastic Cloud Serverless on AWS with Amazon Bedrock as the LLM layer, enabling agentic AI deployments for clients in hours rather than weeks.
Tools & Technologies
Amazon BedrockWhat Leaders Say
“Elastic is a game changer in search accuracy and completeness, especially at a time when organizations want to take full advantage of generative AI.”
“As organizations become more advanced and move into agentic AI use cases, they can add hybrid, vector, and keyword search. That’s where Elastic truly distances itself from virtually every other solution available.”
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Full Story
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.