Vectorize unleashes agentic AI speed and accuracy with Elastic

Vectorize.io, a member of the Elastic AI Ecosystem, is a US-based software company that helps businesses harness agentic and generative AI by making vast amounts of structured and unstructured data usable by large language models (LLMs).

The challenge is especially acute in data-heavy sectors such as law with countless contracts, insurance with many similar policies, and finance with SEC filings or earnings calls. In these fields, search accuracy is non-negotiable. For example, querying "What did Goldman Sachs say on Adobe's Q3 2024 earnings call?" requires precision, not similarity search that might also return Q4 or Q2 results.

Chris Latimer, CEO and founder of Vectorize, says, "Organizations often struggle to combine their data in a way that gives LLMs and AI agents the context needed for accurate, reliable results, especially when deploying retrieval augmented generation (RAG) models."

When researching the market, Latimer says that Elasticsearch was by far the best solution for a hybrid search approach that combines semantic search with traditional methods such as sparse vector search, keyword search, or BM25 algorithms. "Elastic is a game changer in search accuracy and completeness, especially at a time when organizations want to take full advantage of generative AI," he says.

This is where Vectorize excels—scanning across documents, structuring unstructured data, and exposing it to AI agents so they can find exactly what they need.

Vectorize designed its vector data pipelines to connect directly with Elasticsearch, taking advantage of storage, retrieval, and hybrid vector search features to power generative AI and retrieval-based data architectures.

Today, Vectorize runs Elastic Cloud on AWS, with Amazon Bedrock as the LLM or embedding model provider. This delivers a seamless experience for clients already on AWS, enabling them to use data organized in Elasticsearch to build an agentic AI layer for their operations. Their integration with Elastic optimizes their agentic AI supported capabilities, allowing them to become a valued member of the Elastic AI Ecosystem.

Elastic Cloud Serverless plays a key role in Vectorize's IT architecture. "We started with a dedicated cluster, but when Elastic's serverless version arrived, it was an easy decision to switch over," says Latimer. "It's far more cost-efficient and provides many of the capabilities we needed right out of the box, especially multi-tenancy and the ability to spin up indices for our customers."

This flexibility makes Vectorize ideal for businesses that need to launch search projects quickly, with the potential to add features as the scope expands. As Latimer explains, the day-one experience of loading and searching data is simple: "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."

With Elastic, Vectorize can deliver a highly accurate AI solution in just a couple of hours. Building these capabilities in-house, including testing approaches, constructing search indices, and extracting and indexing data fields would typically take at least two weeks of effort.

Another key benefit for customers is accuracy. Vectorize uses Elasticsearch Query Language (ES|QL) to help manage and search large numbers of very similar documents, such as contracts or legal papers, with unfailing accuracy. ES|QL ensures AI agents consistently retrieve the right data, delivering industry-leading precision. This helps businesses quickly find the proverbial "needle in the haystack" within huge volumes of documents.

Elastic's well-established scalability and reliability are also critical advantages for fast-growing Vectorize customers. Latimer gives the example of one client whose user base swelled by a million developers in one year, doubling the community. Vectorize deployed a real-time learning agent to answer queries on the client's Discord support platform.

If the AI agent can't respond, a human steps in. Vectorize immediately captures the response, indexes it in Elastic, and makes it available for future queries in real time. This ability to eliminate delays from data reprocessing or manual index updates highlights how Elastic's scalability and maturity add essential value.

Looking ahead, Elastic's flexibility allows Vectorize to expand its hybrid search, AI, and RAG capabilities so that clients can extract even more value from unstructured data. For instance, Vectorize can process unstructured data (such as support tickets), push it into Elastic, and then build an agentic layer on top.

This layer, called the AI Researcher, acts like an AI employee, enabling different parts of the organization to ask role-specific questions and capture business signals that frontline support might otherwise miss.

Latimer says that by combining Vectorize's data pipelines with the power of Elastic's hybrid search and serverless scalability, organizations can finally unlock the full potential of agentic AI. "What once took weeks of manual effort can now be achieved in hours, with accuracy, speed, and reliability that scale as businesses grow. Together, Vectorize and Elastic are redefining how enterprises turn unstructured data into actionable intelligence."