From Queries to Context: How GoodData AI Understands What You Really Mean

Imagine this scenario: You type a question into a search bar, “What were our top performing regions last quarter?” Within seconds, a chart appears, relevant, clear, and accurate. Analytics, at the speed of thought. Who wouldn’t want that? What seems like a simple interaction is powered by something far more sophisticated: a semantic engine working behind the scenes to understand not just the words you typed, but rather what you meant.

In this article, we’ll take you behind the curtain of GoodData AI’s upgraded semantic search, showing you what has changed, why it matters, and how these invisible upgrades are quietly transforming your experience.

Why Traditional Search Isn’t Enough for Analytics

In many tools, natural language input is still treated like a clever version of keyword search. Type the right words, get lucky, and hope the system recognizes your intent.

But enterprise data isn't that simple.

“Revenue” could mean net or gross, depending on the team. “Top customers” might be sorted by value, frequency, or segment. “Performance” could refer to metrics, KPIs, or even operational benchmarks.

Without a shared understanding of your business logic and terminology, AI-enhanced search becomes a fast but unreliable guessing game.

That’s where semantic search comes in.

What Semantic Search Actually Means

Semantic search is about understanding meaning rather than just matching words.

It works by translating your natural language queries into structured representations that reflect:

• Your data model (facts, dimensions, metrics).
• Your business ontology (e.g., “sales” = SUM(order_amount)).
• User-specific context (e.g., access rights, geography, recent queries).

The result? Instead of finding charts that contain the word “revenue,” GoodData AI understands that you're asking about a specific KPI, with filters and logic already applied.

What We’ve Upgraded and Why It Matters

We’ve made several behind-the-scenes upgrades to improve how GoodData AI interprets and responds to natural language queries:

Smarter Matching with Embeddings

We’ve improved how queries are translated into machine-friendly formats. This enables the system to recognize meaning, even when phrased vaguely or unusually.

Business-Aligned Synonyms

“Revenue,” “sales,” and “turnover” might mean the same thing in conversation, and now they can mean the same thing in your search too, based on your semantic layer and naming conventions.

Hybrid Retrieval

Combining semantic and keyword-based search means users get both relevance and coverage, which is helpful when dealing with partial terms, acronyms, or localized terms.

Relevance Re-Ranking

Multiple potential matches? Our upgraded ranking model surfaces the most contextually appropriate answers based on usage patterns, roles, and data structure.

The Power of Semantic Search: A Before-and-After Comparison

Let’s break down a typical use case to show the difference semantic search can make.

Before (Traditional search):
The User types: “Show me top sales regions last quarter.”
The system looks for keyword matches and returns a static report titled “Sales by Country – 2021 Q4.”
→ The data is outdated, doesn’t match the time range, and misses user intent.

After (GoodData AI with semantic search):
The same query is interpreted using semantic understanding, not just keywords.

Here’s how each phrase is resolved:

• “sales” → mapped to your business-defined Revenue metric (e.g., SUM(order_amount))
• “top regions” → applies a ranking function to Region based on aggregated revenue
• “last quarter” → dynamically calculated as Q1 2025, based on today’s date

GoodData AI returns a fresh, accurate visualization, filtered by the right metric, time period, and ranking. There is no guesswork or outdated dashboards.

The system understands the meaning behind your words, and delivers answers that match your logic, not just your phrasing.

Why These Semantic Upgrades Matter for GoodData AI

These upgrades aren't just about better answers; they’re about enabling an entirely new mode of interaction with data.

With GoodData AI:

• Chatbot queries feel more relevant and personalized.
• Search surfaces the right reports, not just similar ones.
• Future features like anomaly detection and storytelling will rely on the same contextual understanding.

Thanks to oursemantic layer**, your queries** aren’t interpreted by guesswork, they’re grounded in your business definitions, logic, and governance.

Designed for Enterprise-Scale AI

Crucially, these capabilities are built into GoodData AI in a way that respects enterprise-grade expectations:

• Data never leaves your environment: No raw data is ever sent to the LLM.
• Everything is auditable: Each prompt, result, and match is logged.
• White-label ready: Embed this intelligence directly into your apps and workflows.

This is the AI-native layer for analytics: explainable, governable, and made to scale.

The Bottom Line

You don’t always notice a better search engine — you just feel it. When the right answers surface faster, filters apply themselves correctly, and asking a question simply works.

That’s what semantic search is making possible within GoodData AI. And we’re just getting started.

Want to see it in action? Explore GoodData AI today.