On Databases, AI Agents and Security. Q&A with Dave Eyler.
Q1. In your opinion, what are the features and factors that are needed to make a database well-suited to the demands of enterprise AI?
Enterprise AI is already complicated, and the last thing customers need is a bunch of databases making it even more so. Think of a decathlon, where winning requires well-rounded athletes who are exceptional at every sport. Each type of query workload required by Enterprise AI is like a different sport, and only SingleStore has performance to be competitive in all of them.
The database customers need to power enterprise AI needs to be ready for anything. It needs to handle everything from the fast reads and writes required by today’s modern apps to more complex tasks like vector and full-text search, relational and JSON analytics, GPU-accelerated Python, and more. This is why SingleStore stands out – we offer that level of versatility and performance, and at a reasonable price. That sweet spot of price plus performance is something our competitors really struggle with.
Q2. To integrate AI into the operations and process of a large enterprise, there is a proliferation of options and tools. How do you choose them?
I strongly advise organizations to start with the problem, not the tools. Before evaluating any AI solution, clearly define:
● What specific business problems are you trying to solve?
● What are your critical requirements and performance thresholds?
● How will you measure success? As you evaluate options, consider factors beyond just technical capabilities:
● Will it handle your growth trajectory and peak workloads?
● Does it meet your regulatory requirements and data governance needs?
● Can all stakeholder teams effectively utilize the solution?
● Beyond purchase price, what are the operational and maintenance costs?
● How well does it connect with your existing ecosystem?
Don’t get sucked in by the shiniest tools … you’re looking for a way to bring simplicity to the complexity of enterprise AI, and often fancy tools just add more complexity. The best solutions bring simplicity and focus to enterprise AI, consolidating capabilities rather than fragmenting them. The most successful organizations choose tools that solve immediate business problems while providing a foundation for future innovation not technologies that require rebuilding your entire data infrastructure.
Q3. With enterprise AI, simplicity and security are very important. But what is simple and secure?
A single, unified platform that can transact, analyze, and search all your data in a single place.
Anytime you have to move data between systems, things can fall through the cracks. The more you can handle on one platform, the less chance of your data becoming exposed or starting to create hallucinations that lead to less-than-precise results.
So, an ideal solution has an AI model hosting to deploy, fine-tune, and serve LLMs natively. It has real-time vector and hybrid search to deliver AI-driven decisions instantly. It has the streaming data and AI pipelines to process live, dynamic data at scale, and the transactional and analytical capabilities to put an end to batch delays and enable real-time AI.
It also has third-party AI, agents, and app hosting to allow users to run AI agents and services inside their data layer, and a no-code interface to simplify data ingestion from multiple sources.
The less you have to ship your data around, the simpler and secure your solution becomes. One and done is the best way.
Q4. And in your opinion what’s not simple and secure?
The way most companies are doing it is backward engineering old or legacy systems in an attempt to meet the new realities of AI. So many organizations are in this boat: cobbling together a patchwork assortment of legacy architectures that were never intended for real-time AI. They’re relying on a grab bag of databases, vector stores, analytics engines, and a model-serving infrastructure–and the drawbacks to such an assemblage are considerable. Such an arrangement only serves to slow down the AI, drive up costs, and limit innovation.
Q5. You mentioned a so-called “single-shot retrieval”. What is it? What are the main benefits?
The easiest way to explain single-shot retrieval is to first show what it isn’t.
Not Single-shot retrieval
Look at how all those calls to LLMs and different databases add up! Not fast, not simple, and not secure.
Single-shot retrieval is our approach that enables AI agents to execute comprehensive, multi-step data retrieval operations in a single query. Rather than making multiple separate queries to different data stores, the agent composes one intelligent query that navigates through various data types and structures to gather all necessary context in a single operation.
Technically, this works because SingleStore can process diverse data types – structured tables, documents, vectors, spatial data, time series – within a unified environment. The query automatically implements the appropriate processing hierarchy, applying the right techniques to each data type while maintaining the relationships between them.
By eliminating multiple query roundtrips, we drastically reduce latency. AI responses that previously took seconds to fetch from multiple sources, now happen in milliseconds. Developers no longer need to orchestrate complex sequences of queries across different databases, dramatically reducing code complexity. AI agents receive all relevant information, leading to more accurate, contextually rich responses. Data never leaves your secure environment, eliminating vulnerability points during transfers between systems. Your team manages one platform instead of multiple specialized databases.
This is only possible on SingleStore because we uniquely combine multimodal data handling with the performance necessary to execute these complex operations in real-time. It allows organizations to push the boundaries of AI performance without the traditional trade-offs and complexity of integrating multiple specialized data platforms.
Q6. Can you detail more about what role an AI agent has in this context?
An AI agent in this context acts as an intelligent intermediary between users and data. Think of it as a sophisticated assistant that can understand natural language requests, determine what information is needed to fulfill them, and then orchestrate the necessary data operations to deliver results.
Without SingleStore’s single-shot retrieval capability, agents face significant challenges. For example, The agent receives a user query like “How did our Q1 sales in Europe compare to last year, and what factors drove the biggest changes?”
1. To answer this comprehensively, the agent would need to:
○ Query a transactional database for current sales figures
○ Access an analytics database for historical comparisons
○ Pull relevant documents from a document store
○ Search customer feedback in a text database
○ Retrieve similar patterns from a vector database
2. Each languages, and performance characteristics, dramatically complicating the agent’s task and introducing latency.
With SingleStore’s single-shot retrieval, the agent simply formulates one comprehensive query that pulls all this diverse information in a single operation. The agent becomes significantly more powerful because it’s no longer constrained by infrastructure limitations.
Returning to my decathlon analogy – just as a decathlon champion needs across-the-board excellence, an effective AI agent needs comprehensive access to all relevant data types. SingleStore ensures your agent has this complete context delivered with exceptional speed, making it truly capable of handling the complex, multifaceted questions that drive enterprise value.
Q7. When an AI agent is involved in a retrieval how do you guarantee consistency and quality of the results?
This is a profound question that gets to the heart of one of the biggest challenges in AI today. I believe that the truth is that no platform can absolutely guarantee perfect consistency and quality from probabilistic AI models like LLMs. These models are inherently statistical in nature, and the industry is investing enormous resources into addressing issues like hallucinations and inconsistency.
By providing comprehensive, accurate context from authoritative data sources in real-time, we dramatically reduce the likelihood of hallucinations. When an LLM has the right information within threshold duration for responding, it’s far less likely to fabricate answers.
When pulling information from different sources (transactions, documents, vectors), we preserve the relationships between these elements, ensuring the agent receives a coherent, consistent view of your data.
We are actively working on making it easy to trace exactly what data was used to generate each response. This evidence-based approach allows organizations to validate outputs and build confidence in the system.
of these operations requires separate connections, authentication, query
While we can’t claim to completely solve the inherent probabilistic nature of LLMs, we provide the most robust foundation possible: comprehensive, consistent, high-quality data delivered in real-time. This context-rich environment is the best defense against hallucinations and inconsistency in AI responses.
Q8. Why not use a dedicated vector database instead?
Using a dedicated vector database might seem appealing if vector similarity search is your only requirement, but this approach creates significant problems for enterprise AI deployment.
First, vector databases are purpose-built for a single function – similarity search. But real-world AI applications require much more: transactional capabilities, traditional analytics, full-text search, geospatial operations, and more. With a dedicated vector database, you’d need to add specialized databases for each of these functions.
Each database requires its own integration, replication, and synchronization strategies. Your engineering effort ends up focused on building and maintaining complex data pipelines instead of delivering Business AI value. Each additional database requires its own monitoring, backup, security, and update procedures. This multiplies operational costs and creates management headaches. Moving data between systems introduces latency that makes real-time AI applications impossible. What good is fast vector search if you still need to join those results with data from your transactional system?
Organizations using our approach report significant reductions in complexity, faster time to market, and lower total costs compared to multi-database architectures.
Q9. Anything else you wish to share?
Organizations that build their AI initiatives on fragmented, complex infrastructures are discovering that these architectures become significant barriers to scaling and evolving their capabilities.
What we’re seeing among market leaders is a strategic shift toward simplification and consolidation. SingleStore is uniquely positioned at this intersection of simplicity and capability. We’ve worked with numerous Fortune 500 companies who initially attempted to build on patchwork architectures before realizing the limitations of that approach.
Looking ahead, I believe we’re still in the early stages of enterprise AI adoption. The organizations that will gain sustainable competitive advantage are those that build on flexible, scalable foundations that evolve with rapidly changing AI technologies. Our mission is to provide that foundation – one that’s simple enough to accelerate time-to-value today, yet powerful enough to support whatever comes next in the AI revolution.
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Dave Eyler is Vice President of Product at SingleStore. He has extensive experience in data management, databases, analytics, software engineering, and mobile.
Sponsored by SingleStore