From Code to Conversation: The AI Agent That Speaks Database

Fortune 500 companies are transforming enterprise data access with AI that turns natural conversation into precise database queries—and it's already reshaping how businesses interact with their data.

Imagine watching Carlo Ancelotti prepare for a crucial match at Real Madrid's training ground. Just days after a stinging 5-2 defeat to Barcelona in the Spanish Super Cup final, every training decision matters doubly. (Perhaps with faster access to defender fatigue data, that scoreline might have looked different—though even the best AI can't stop Lewandowski on a good day.) But instead of waiting for complex database queries from the analytics team, picture Ancelotti simply asking aloud, "Show me yesterday's distance coverage for all defenders." Within seconds, he has a ranke[d list of his back four's fitness levels.

While this might sound like a football fantasy, similar transformations are already happening in enterprise security operations centers worldwide. At Vulcan Cyber, one of the leading vulnerability management platforms, security administrators type "show critical vulns on windows servers from last week" and instantly receive a comprehensive vulnerability report—no complex filtering menus or technical queries required. This seamless interaction between human and data isn't just transforming how enterprises operate—it's what we've built at Neradot.

The Database Babel Fish

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For decades, accessing corporate data has been like ordering coffee in a foreign language—one wrong syllable and you might end up with something entirely different from what you wanted. Traditional database queries require exact syntax, precise parameter names, and intimate knowledge of data structures. It's a system that works perfectly for machines but terribly for humans.

Our solution, powered by our Neralbs framework, functions like Douglas Adams' fictional Babel fish—a universal translator between human intention and database queries. But unlike simple translation tools, it handles everything from formal requests ("Show me Q4 sales figures for the Northeast region") to abbreviated demands ("ords Dec 24") with consistent reliability.

Enterprise-Ready Intelligence

For enterprises looking to unlock their data's potential, our Text-to-Query solution comes as part of Neradot's broader data intelligence practice. What makes this particularly powerful is how it integrates with existing enterprise systems. Companies don't need to rip and replace their current databases or retrain their teams - it layers seamlessly on top of existing infrastructure.

This seamless integration has proven transformative for companies like Vulcan Cyber.

"Our customers love our comprehensive filtering capabilities, but we recognized an opportunity to make this power more accessible"

says Tal Marom, VP Product at Vulcan Cyber.

"By partnering to develop this natural language interface, we're transforming how security teams interact with their vulnerability data, making sophisticated queries as simple as having a conversation.”

The solution can be deployed either as a managed cloud service or on-premises, crucial for Fortune 500 companies with specific security and compliance requirements. With pricing based on query volume and complexity, organizations can start small and scale as needed.

When Theory Met Reality

The real test came during our beta testing with Fortune 500 clients. Early results revealed an unexpected challenge: while we initially expected users to formulate "parameter queries" (e.g., "Server with name 'MyFastMachine'"), they actually typed things like "show FastMachine." It was a humbling reminder that we needed to build for real users, not our idealized versions of them.

"We had to unlearn our assumptions about how people would interact with data," our lead engineer reflects. "Users don't think in parameters and schemas—they think in goals and outcomes."

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The Technical Tightrope

What makes our achievement particularly meaningful is our solution to the "nested NoSQL schema" problem—imagine trying to navigate a Russian nesting doll where each layer has its own set of rules, and those rules change depending on what's inside the previous doll. Traditional approaches would crumble under such complexity, but our system maintains 94% accuracy offline and 90% in production environments.

Our approach combines three critical innovations:

• A comprehensive prompt engineering framework that adapts to user behavior
• A schema validation system that catches errors before they propagate
• A hybrid feedback loop incorporating both automated checks and human input

The Business Impact

For enterprises working with us, the implications have been staggering. Our early adopters report:

• 90% reduction in time-to-insight for data queries
• Significant decrease in load on technical support teams
• Democratized data access across organizational hierarchies

All this comes with an average latency of 2.5 seconds and a cost of approximately one cent per query—numbers that make CFOs as happy as the end users.

Looking Ahead

As impressive as natural language querying is, we see it as just the beginning. We're already exploring contextual awareness for more natural conversations, predictive suggestions based on user patterns, and cross-platform implementations.

The days of technical gatekeeping for data access may be numbered. As one of our beta testers put it, "It's like having a data analyst in your pocket who actually understands what you're asking for."

And for that football manager's dream? While Real Madrid may not be using our system yet, enterprises worldwide are already seeing the benefits of speaking data's language naturally. Sometimes, the future of technology isn't about building something new—it's about making what we already have feel natural.

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^{Deep Dive: The Technical Architecture
For those who are interested in the technical implementation details of Neradot's Text-to-Query system, including our approach to prompt engineering, schema validation, and NoSQL architecture, we've published a comprehensive technical case study on our engineering blog: Building Natural Language Interface for Vulcan Cyber ExposureOS.}