From Filters to Natural Language: How AI Is Changing the Way We Query Data

Bringing you closer to data through AI-powered natural language interfaces and smart, adaptive user experiences.

From Natural Language to Query: Unlocking data access with AI that understands both people and systems.

In today’s data-driven world, organizations often have powerful back-end systems and rich data stores — yet many users lack the technical expertise needed to query them. What if you could simply ask in plain language — “Show me customers who purchased product X last month in Madrid” — and have that translated into the correct query syntax behind the scenes?

At Nieve Consulting, we’ve already done exactly that for our client: building a service that accepts natural language input and translates it to their proprietary query language, enabling nontechnical users to retrieve data more intuitively.
👉 Read about the Custobar project.

But the possibilities go far beyond that single integration. Let’s look at how this capability is reshaping the way teams access, understand, and use data — and where we can take it next.

Why “Natural Language → Query” Is a Game Changer

1. Lower the barrier to insights
   Business users — from marketing to operations — often depend on data teams to write queries or dashboards. Natural language interfaces democratize access, allowing anyone to explore data.
2. Speed and agility
   Instead of waiting for BI or engineering support, users can iterate faster, asking ad hoc questions in real time.
3. Reduce errors and ambiguity
   Embedding domain knowledge and validation logic in the translation layer reduces malformed queries and ensures meaningful results.
4. Scalable across tools
   Once built, a robust translation engine can adapt to SQL, GraphQL, Elasticsearch, or domain-specific languages — enabling unified access across systems.

Bridging Filters and Natural Language: Smarter User Interfaces

One of the most promising developments lies in interfaces that fluidly combine traditional filter-based query builders with AI-powered natural language search. This dual approach ensures both beginners and experts can interact with data in the way that feels most natural.

• From Filters to Natural Language
  Imagine a marketing analyst applying filters like “Country = Spain,” “Product = Subscription A,” and “Date = Last 30 days.”
  The system could automatically generate the natural language equivalent:
  “Show me all Spanish customers who purchased Subscription A in the last 30 days.”
  This helps users learn how to phrase queries and confirm that the filters were correctly interpreted.
• From Natural Language to Filters
  Conversely, a manager might type:
  “Which customers in Helsinki purchased premium services in September?”
  The system could instantly convert this into visible filters — Location = Helsinki, Product Tier = Premium, Date = September — giving users both transparency and control.
• Adaptive Interfaces for All Skill Levels
  Over time, the system learns how users prefer to interact. Some lean on natural language; others prefer filters but occasionally type queries. The result: a personalized experience that bridges human expression and data precision.

This two-way interaction — filters ↔ natural language — builds user trust and bridges the gap between intuitive querying and precise control.

Potential Extensions and Future Projects

Key Technical and Organizational Considerations

Building a natural language query system isn’t just an AI challenge — it’s a design, data, and governance challenge too.

• Schema and metadata awareness
  The translation engine must understand how your data is structured — tables, relationships, and data types — to produce valid queries.
• Ambiguity resolution and clarification
  Input like “active users last month” can mean many things. The system must ask clarifying questions or apply smart defaults.
• Latency and performance
  Query translation should feel instant. Caching and optimization are critical to maintaining user flow.
• Security and permissions
  The system must respect access rights, prevent leaks, and guard against unsafe queries.
• Hybrid architecture (LLM + rules)
  Combining large language models with deterministic logic ensures reliability and explainability — key for production systems.
• Continuous improvement
  Tracking accuracy, logging feedback, and measuring user satisfaction are essential for long-term success.

Why Nieve Is Well-Positioned to Lead This Space

At Nieve Consulting, we’re already turning this vision into reality.

• We’ve built a production-grade AI query translator for Custobar, proving real-world viability.
• Our team combines expertise in data engineering, machine learning, API design, and user experience — enabling holistic solutions that work end-to-end.
• We can start small — a single dataset or team — and scale to full enterprise implementations.
• We are domain-agnostic: whether it’s e-commerce, logistics, energy, or finance, we build interfaces that make complex data approachable.

The Future of Data Access

With AI bridging filters and natural language, businesses no longer have to choose between precision and ease of use.
The future of querying is conversational, visual, and adaptive — empowering every user, from analysts to executives, to get the insights they need simply by asking.

At Nieve, we believe this is more than a UX upgrade — it’s the next step in making data truly accessible to everyone.