While vector databases are becoming essential in enterprise AI deployments for Retrieval Augmented Generation (RAG), the real challenge lies in managing unstructured data effectively.
Chris Latimer, co-founder and CEO of Vectorize, previously led cloud initiatives at DataStax. He observed a common issue: the vector database itself wasn't the primary hurdle in enterprise RAG implementation. Instead, the difficulty was in optimizing the ingestion of unstructured data into the vector database to benefit generative AI.
In response, Latimer launched Vectorize ten months ago to address this challenge. The company has now announced a $3.6 million seed funding round led by True Ventures, alongside the general availability of its enterprise RAG platform. This platform facilitates an agentic RAG approach, enabling near real-time data capability. Vectorize focuses on data engineering, helping organizations prepare and manage their data for vector databases and large language models (LLMs). Moreover, it allows enterprises to swiftly build an RAG data pipeline through an intuitive interface and features an RAG evaluation tool for testing various strategies.
“We consistently found that at the final stages of Gen AI projects, the results often fell short,” Latimer noted in an exclusive interview. “The context provided to the vector database was not useful for the large language model, leading to hallucinations and misinterpretations of the data.”
How Vectorize Integrates into the Enterprise RAG Stack
Vectorize is not a vector database; it is a platform that connects unstructured data sources to existing vector databases like Pinecone, DataStax, Couchbase, and Elastic. It ingests and optimizes data from varied sources, ensuring a production-ready data pipeline that encompasses ingestion, synchronization, error handling, and best practices in data engineering.
Additionally, Vectorize is not a vector embedding technology. Instead, it supports users in evaluating different embedding models and data chunking methods to discover the optimal configuration for their specific use cases. Latimer highlighted the flexibility of the platform, allowing users to choose from numerous embedding models, including OpenAI’s Ada and Voyage AI embeddings used by Snowflake.
“We focus on innovative data vectorization strategies to yield the best results,” Latimer said, emphasizing that the platform provides a production-ready solution, alleviating concerns over data engineering.
Leveraging Agentic AI for Enterprise RAG
A standout feature of Vectorize is its “agentic RAG” approach, which combines traditional RAG methods with AI agent capabilities, fostering autonomous problem-solving. Groq, an early adopter and AI inference silicon startup that recently secured $640 million, employs Vectorize’s agentic RAG capabilities to enhance an AI support agent. This agent can autonomously solve customer inquiries using data and context from Vectorize’s pipelines.
Latimer explained, “If a customer asks a recurring question, the agent should efficiently resolve their issue without human intervention. However, if it encounters a more complex problem, it should escalate to a human for assistance, embodying the essence of an AI agent architecture.”
The Importance of Real-Time Data Pipelines in Enterprise RAG
For enterprises, a significant advantage of utilizing RAG is accessing up-to-date data. “Stale data results in poor decision-making,” cautioned Latimer. Vectorize offers real-time and near-real-time data updating capabilities, allowing customers to customize their data freshness preferences.
“We empower users to configure the platform according to their acceptable levels of data staleness,” he said. “Whether they require weekly data refreshes or real-time updates, our platform can accommodate those needs, providing timely updates as data becomes available.”
Ranking
