Introducing Actian VectorAI DB: AI Where Your Data Lives

AI is entering a new phase. The era of prototypes is ending, and the era of production systems is beginning. Over the past two years, organizations have experimented with generative AI, built pilots, and explored what large language models can do. Now that the focus is shifting toward production, teams are asking a much harder question: what infrastructure is required to make AI reliable at enterprise scale?  The answer increasingly comes down to the data layer. 

Modern AI systems depend on fast, intelligent access to data. Semantic search, retrieval-augmented generation (RAG), and AI agents all rely on the ability to retrieve relevant information quickly and ground their responses in enterprise knowledge. When retrieval works well, the system feels intelligent. When it does not, chaos ensues. 

At the same time, the environments where AI systems run are becoming more distributed. Enterprise data rarely lives in one place. It spans cloud platforms, operational systems, data centers, and edge environments where data is generated close to machines and applications. Regulatory requirements, privacy constraints, and internal governance policies often dictate where that data must reside. In other words, the next generation of AI infrastructure will not be centralized. It will be distributed, governed, and deployed wherever data lives. 

Today, we are introducing the Actian VectorAI DB, a vector database designed to support this new generation of AI systems by enabling fast semantic retrieval across cloud, on-premises, and edge environments.  The launch coincides with the release of my new O’Reilly report, “Vector Databases for Enterprise AI”, which explores why vector retrieval is becoming a foundational capability for enterprise AI and how distributed architectures are reshaping the design of AI data platforms. 

Resumen

• AI systems increasingly depend on vector search to retrieve context for RAG pipelines, AI assistants, and agent-driven workflows.
• Many vector databases assume centralized cloud architectures that do not fit regulated or edge environments.
• Actian VectorAI DB enables high-performance semantic retrieval with a lightweight, low-memory footprint across edge, on-premises, hybrid, and cloud deployments.
• The goal is simple: bring AI closer to the data it depends on instead of forcing data into centralized systems.

Why Vector Databases are Becoming Essential to Enterprise AI

Since the launch of ChatGPT in 2023, organizations have experienced an unprecedented surge in experimentation with generative AI. As new use cases emerge, however, so do new risks. High-profile security incidents have highlighted the challenges of managing sensitive data in AI-driven environments, while organizations increasingly recognize that AI systems must retrieve and interact with enterprise knowledge in a controlled and reliable way. 

At the same time, regulatory pressure is accelerating. Between 2023 and 2024, more than 170 new privacy laws¹ were introduced worldwide, and the EU AI Act signals a shift toward stricter oversight of how AI systems are developed and used. As AI moves from experimentation into production, businesses need greater control over how data is accessed, retrieved, and used by AI systems. 

One of the most important changes in modern AI architectures is how these systems retrieve knowledge. 

Machine learning models convert information such as documents, images, conversations, and other unstructured content into numerical vectors known as embeddings. These embeddings capture semantic meaning rather than literal structure. A vector database indexes those embeddings and allows systems to search for similar vectors so that applications can retrieve information that matches the intent of the query rather than a keyword. 

Why Centralized Architectures Break Down

Most vector databases assume centralized cloud infrastructure. In practice, many enterprise AI systems operate in environments where those assumptions do not hold. 

Manufacturing systems analyze sensor data directly on production lines. Healthcare systems process sensitive patient information within regulated environments. Financial institutions operate under strict data residency rules that limit where data can move. Government systems frequently operate in disconnected or restricted networks. 

In these scenarios, moving large volumes of data to centralized infrastructure is often impractical or prohibited. AI systems must instead run close to the data they depend on. 

Introducing Actian VectorAI DB

Actian VectorAI DB is designed specifically for those environments where AI systems must run close to the data they depend on. It provides native vector storage and high-performance similarity search using techniques such as approximate nearest neighbor (ANN) indexing. Algorithms such as HNSW enable efficient retrieval across large embedding collections while balancing speed, accuracy, and resource usage. More importantly, Actian VectorAI DB is designed to run wherever AI applications need it. The system supports deployment across embedded systems, edge environments, on-premises infrastructure, hybrid architectures, and cloud platforms. 

Developers can build AI applications once and deploy them across environments using the same architecture and APIs. This eliminates the need to redesign the retrieval infrastructure as systems move from prototype to production. The result is a portable vector database that allows organizations to bring AI closer to their data rather than forcing data into centralized platforms. 

To understand how vector retrieval fits into modern AI systems, it helps to look at the architecture of a typical AI application. Enterprise data sources such as documents, operational records, knowledge bases, and application metadata are transformed into embeddings and indexed for similarity search. AI frameworks and models then interact with this retrieval layer through APIs, connectors, and application integrations, allowing applications such as search, RAG systems, copilots, and AI agents to retrieve relevant context in real time. 

The diagram below illustrates how Actian VectorAI DB sits at the center of this architecture, enabling semantic retrieval while supporting deployment across cloud, hybrid infrastructure, on-premises environments, and edge devices. 

What Actian VectorAI DB Enables

Fast semantic retrieval opens the door to a wide range of AI applications. Enterprise search systems can retrieve knowledge from large knowledge bases based on meaning rather than keywords. RAG pipelines can ground large language models in proprietary enterprise data. AI assistants and agents can retrieve contextual information needed to support reasoning and decision-making. 

Vector similarity can also support anomaly detection, recommendation systems, and multimodal search across text, images, and other forms of unstructured data. As these applications become more autonomous, the ability to run vector infrastructure across distributed environments becomes increasingly important. 

The Future of AI Infrastructure

For many years, the dominant architectural trend in data platforms was centralization. Data was collected and consolidated into large platforms where analytics and machine learning could run.  AI is now pushing the industry toward a different model. 

Intelligence increasingly runs wherever data is created, whether that is inside enterprise systems, edge environments, or distributed applications. Vector databases are emerging as one of the key building blocks of that architecture because they enable systems to retrieve knowledge based on meaning rather than structure. 

Actian VectorAI DB is designed for that future. 

It provides a portable vector database that enables semantic retrieval wherever AI applications run while giving organizations control over where their data resides and how it is processed.  Because the future of AI will not run in one place. It will run wherever data lives. And the infrastructure that supports it must be built for that reality. 

If you are exploring how vector retrieval can support production AI systems, we invite you to start building with Actian VectorAI DB today. Try it for free and see how high-performance semantic retrieval can power your own AI applications. 

You can also join the conversation with other developers and practitioners in our Actian Developer Discord community, where teams share ideas, ask questions, and explore new AI use cases together. 

Start building with Actian VectorAI DB today. Try it for free and see how high-performance semantic retrieval works in your own AI applications.

Join the conversation with other developers and practitioners in our Discord community to share ideas, ask questions, and explore new use cases together.

¹Source: Graham Greenleaf, “Global Data Privacy Laws 2025: 172 Countries, Twelve New in 2023/24,”  Macquarie Law School, April 2, 2025

Acerca del autor

Acerca de Emma McGrattan

Emma McGrattan es CTO en Actian, liderando la I+D global en análisis de alto rendimiento, gestión de datos e integración. Con más de dos décadas en Actian, Emma tiene múltiples patentes en tecnologías de datos y ha sido fundamental en el impulso de la innovación para aplicaciones de misión crítica. Es una autoridad reconocida, con frecuentes ponencias en conferencias del sector como Strata Data, y ha publicado artículos técnicos sobre analítica moderna. En sus publicaciones del blog de Actian, Emma aborda la optimización del rendimiento, las arquitecturas de nube híbrida y las estrategias de análisis avanzadas. Explore sus artículos principales para desbloquear el éxito impulsado por los datos.