Does Lumenore provide an option to export AskMe charts and grids?

I would like to understand whether Lumenore Ask Me supports classification-type analysis based on historical data patterns.

For example, scenarios such as:

• Segmenting entities into risk categories
• Classifying records into performance tiers
• Identifying stable vs. churn risk customers
• Categorizing behavior patterns based on historical trends

Could you please clarify:

• Can I perform classification analysis directly using NLQ prompts?
• Is a predefined target or labeled field required for such analysis?
• Does Lumenore Ask Me automatically detect patterns and create classifications, or does this need to be pre-modeled within the dataset?
• What data preparation or schema considerations are necessary to enable reliable classification insights?

My objective is to understand the capabilities and limitations of classification analysis within Lumenore Ask Me and how best to structure data to support it.

Hello Team,

I would like to understand how forecasting can be performed using Lumenore Ask Me, based on historical trend analysis.

My dataset includes:

• A Date field (Year / Month / Transaction Date)

• Business metrics such as Sales and Revenue

• Supporting dimensions like Region and Product

Use case:
I want to leverage past performance data to:

• Forecast future monthly sales

• Predict revenue trends for upcoming quarters

• Identify expected growth or decline based on historical patterns

Before implementing this, I would like clarification on:

• Does Lumenore Ask Me support built-in forecasting based on historical trend analysis?

• What data preparation or schema considerations are required to enable accurate forecasting?

• Is a specific time hierarchy (Year → Month) required for reliable projections?

• Are there best practices for ensuring the forecast output is meaningful and statistically sound?

• Can forecasting be triggered through NLQ queries (e.g., “Forecast sales for the next 6 months”)?

My objective is to enable business users to analyze historical trends and generate forward-looking insights confidently through Lumenore Ask Me.

I am planning to leverage Lumenore Ask Me for the analytics and would like to ensure that my dataset is properly structured to achieve a high AI Score and deliver accurate, context-aware responses.

My dataset contains transactional data such as:

• Date fields
• Categorical dimensions (Region, Product, Category, etc.)
• Business metrics (Sales, Revenue, Quantity, etc.)

In addition to making the dataset NLQ-ready, I would like guidance on how to further optimize it so that Lumenore AI can better understand user intent and provide more precise answers.

Specifically, I would like to understand:

• What modeling best practices improve AI Score in Lumenore Ask Me?
• Are there recommended naming conventions or business-friendly labels that enhance NLP interpretation?
• How should measures, calculated fields, and date hierarchies be structured for better AI understanding?
• Does adding descriptions, synonyms, or metadata improve AI performance?
• What common data preparation mistakes negatively impact AI Score?

My objective is to enable business users to ask questions such as:

• “Show sales by region for 2023”
• “What is the YOY growth in revenue?”
• “Top 5 products by sales last quarter”

I would appreciate any best practices or recommendations to align my dataset with Lumenore Ask Me interpretation model and improve both AI Score and response accuracy.

Is the entire underlying dataset exposed to the LLM in order to generate insights?

If so, is any portion of that data stored, retained, or used for training by the LLM provider?