Core Feature

Natural Language Queries
Will Blow Your Mind

Transform technical database queries into natural conversation. Ask sophisticated analytical questions using business terminology and get accurate results without needing to understand SQL, APIs, or complex data structures.

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How Natural Language Queries Works

Ask complex data questions using plain English without SQL or technical knowledge.

1

Speak Your Question

Ask your data question using natural business language and terminology you already know.

2

AI Understands Intent

Advanced NLP interprets your question and translates it into the appropriate data operations.

3

Get Precise Results

Receive accurate data results with explanations of how the analysis was performed.

Plain English Data Access

Natural Language Queries eliminate the technical barriers between you and your data. Ask sophisticated analytical questions using the same business terminology you use every day, and get precise results without needing to learn SQL, understand database schemas, or navigate complex interfaces. dashboard example

Query Capabilities

Simple Aggregations

Ask straightforward questions about your data:

  • "What was our revenue last month?"
  • "How many new customers did we acquire this quarter?"
  • "Which product had the highest sales yesterday?"
  • "What's our average order value this year?"

Complex Comparisons

Perform sophisticated comparative analysis:

  • "Compare Q3 performance to the same period last year"
  • "Show revenue by region for mobile vs desktop users"
  • "How do conversion rates differ between marketing channels?"
  • "Which customer segments have the highest lifetime value?"

Time-Based Analysis

Explore trends and patterns over time:

  • "Show me monthly growth trends for the past year"
  • "What's our week-over-week change in user engagement?"
  • "How does seasonal performance compare to previous years?"
  • "When do we typically see our highest sales volumes?"

Advanced Natural Language Features

Context Awareness

The system maintains context throughout your session:

  • Reference previous queries without repeating full context
  • Build complex analyses through iterative questions
  • Remember definitions you've established in conversation
  • Understand pronouns and references to earlier results

You can add context on Workspace, Source or User level.

  • Click on Memories from the left sidebar. dashboard example

  • Then Click on New Memory dashboard example

  • Select your Level (Workspace/User/Source) describe your context dashboard example

Fuzzy Matching

Flexible interpretation handles real-world language:

  • Spelling variations and typos in metric names
  • Synonym recognition for business terms
  • Partial matches when you're not sure of exact names
  • Contextual disambiguation when terms could mean multiple things

Multi-Source Queries

Ask questions that span multiple data sources:

  • "Combine CRM data with website analytics"
  • "Show social media performance alongside sales data"
  • "Compare advertising spend to revenue generation"

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Frequently Asked Questions

How complex can my natural language queries be?

You can ask very sophisticated questions involving multiple data sources, time comparisons, statistical analysis, and complex business logic. The system handles multi-step queries like 'Compare Q3 revenue by product line to last year, excluding discontinued items, and show statistical significance.'

What if the system doesn't understand my question?

The AI will ask clarifying questions to better understand your intent. You can rephrase using different business terms, break complex questions into smaller parts, or use the suggested question prompts that appear based on your data structure.

Can I use industry-specific terminology?

Absolutely! The system learns and adapts to your business vocabulary and industry terminology. The more you use specific terms for your metrics, products, or processes, the better it becomes at understanding your unique business context.

How does this compare to writing SQL queries myself?

Natural language queries are much faster for exploration and accessible to non-technical users, while custom SQL gives you precise control. Many users start with natural language to explore and understand data, then move to custom SQL for production reports and complex analysis.

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