AI Enrichments

Why AI Enrichment Matters

Exploratory analysis provides answers, but deeper insight often requires creating new columns, classifying values, or enriching existing data with advanced intelligence. Traditionally, these tasks involve writing formulas, scripts, or running models in external tools.

askEdgi integrates these capabilities directly into the Workspace through AI Functions. Using natural language prompts, new calculated columns can be created, records classified, or text data analyzed without the need for external scripting or additional tools.

What Can Be Done

AI Functions provide the ability to:

• Create calculated columns (e.g., profit margin percentage)

• Perform classifications (e.g., High/Medium/Low risk)

• Apply text-based analysis techniques, including sentiment analysis, intent detection, emotion recognition, and classification, to analyze text data.

Use Case & Real-Life Scenario

Continuing the return analysis, a product manager explores severity levels using AI enrichment:

“Create a new column for return_rate = (returns/orders) * 100.

askEdgi generates a calculated column return_rate for each product.

“Classify products into High, Medium, or Low return categories based on return_rate (High: >30%, Medium: 10–30%, Low: <10%).”

askEdgi creates a new column return_category, to highlight product risk levels.

“Perform sentiment analysis on customer_reviews.”

askEdgi enriches the dataset with a sentiment_label column (Positive, Neutral, Negative).

By combining return patterns with sentiment insights, the analysis reveals that products with negative sentiment also report the highest return rates, signaling potential quality issues.

Different AI enrichments

Prompt Analysis Evaluates the clarity and effectiveness of user-generated prompts to ensure accurate results.

Example: Generate a new column comparing income in the dataset against the average income to provide deeper insight into earning levels.

Sentiment Analysis Classifies text data into Positive, Neutral, or Negative categories.

Example: Analyze customer reviews, browsing history, and purchase records to gain insights into customer behavior.

Intent Analysis Identifies underlying intent in textual data, classifying it into predefined categories.

Example: Detect intent in customer support or compliance interactions.

Emotion Analysis Detects emotional tones in text for better understanding of customer experiences.

Example: Assess emotions in product reviews or support conversations.

Text Classification Categorizes text into domain-specific classes such as fraud detection or spam filtering.

Example: Apply classification models to incoming emails or financial records to automate data analysis and processing.

Proofreading Identifies grammatical, clarity, and structural issues to ensure professional communication.

Example: Refine business documents or product descriptions for improved readability.

Availability

• Public – Available (AI column creation and enrichment)

• SaaS – Available (full AI functions)

• On-Prem – Limited (Metadata analytics only)

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