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)

Retrieval Augmented Generation (RAG) in askEdgi

Retrieval Augmented Generation (RAG) in askEdgi refers to answering questions by first retrieving trusted enterprise context and then generating responses based on that context.

askEdgi is not a generic AI chatbot.

It does not rely on assumptions or general knowledge.

Instead, askEdgi:

• Understands business terms defined by the organization

• Knows what data exists and how it is governed

• Considers structural relationships between datasets

• Uses metadata, lineage, and contextual statistics

• Suggests relevant and trusted assets before execution

• Explains answers using enterprise context

This approach ensures that responses are grounded in how the organization understands and manages its data.

Business Value of RAG

Business users frequently need answers to questions such as:

• Which dataset should be used for a metric?

• What does a business term mean?

• Where does a number originate?

• Why do different reports show different values?

• Which datasets should be combined?

These questions require understanding business meaning, structural relationships, and governance alignment.

Retrieval Augmented Generation ensures:

• Accurate answers

• Consistent interpretation

• Connected datasets

• Trustworthy analysis

Enterprise Context Used by RAG

The enterprise context retrieved by askEdgi includes the following elements.

Context Type	Description
Business glossary	Approved business definitions and terminology
Curated datasets	Trusted and governed data assets
Governance information	Ownership, classification, and access controls
Metadata and documentation	Business and technical descriptions
Dataset relationships	Structural connections between assets
Lineage information	Data movement and dependency paths
Contextual statistics	Sample characteristics and value distributions
Top values	Frequently occurring values used for interpretation

Sample statistics and top value summaries improve interpretation but remain secondary to governed metadata and definitions.

askEdgi Modes and the Role of RAG

askEdgi operates in two clearly defined modes to support different user needs. Each mode has a clear purpose and boundary that ensures trust and predictability.

Analysis Mode

Analysis Mode is the default and primary mode. It supports the complete journey from understanding a question to generating insights.

Purpose of Analysis Mode

Analysis Mode supports the following activities:

• Understanding a question

• Identifying the correct data

• Validating how datasets relate

• Performing analysis

• Receiving business-aligned explanations

Analysis Mode supports the complete journey from discovery to insight without switching between guidance and execution modes.

Understand RAG Usage in Analysis Mode

In Analysis Mode, Retrieval Augmented Generation performs the following functions:

• Interpret the business meaning behind a question

• Retrieve relevant glossary definitions

• Surface business and technical descriptions of assets

• Evaluate asset metadata and documentation

• Analyze relationships between datasets

• Eliminate unrelated or disconnected tables

• Confirm that selected datasets combine correctly

Retrieval Augmented Generation ensures reasoning in a business context before execution begins.

Understand Relationship Aware Intelligence

When a question spans multiple datasets, askEdgi performs structural validation.

askEdgi performs the following actions:

• Confirm that selected assets are structurally connected

• Avoid a combination of unrelated datasets

• Suggest additional related assets only when required

• Limit context expansion to what is necessary

This prevents:

• Incorrect joins

• Over-selection of irrelevant tables

• Misleading analysis

• Loss of trust

Datasets are validated as part of a connected data ecosystem rather than isolated objects.

Understand Workspace First Execution

Analysis Mode respects the Workspace as the execution boundary.

Execution rules are as follows:

• If tables remain pinned, analysis is restricted to pinned tables

• If tables are not pinned, eligible workspace tables are considered

• Additional catalog assets are surfaced only when necessary

• Data outside the intended scope is not analyzed

This ensures controlled execution.

Discovery Mode

Discovery Mode supports structured exploration of the Data Catalog. This mode does not use RAG and does not perform analysis execution.

Purpose of Discovery Mode

Discovery Mode supports:

• Asset browsing

• Data availability validation

• Metadata understanding

• Documentation review

Understand Discovery Mode Behavior

In Discovery Mode, askEdgi performs the following actions:

• Retrieve assets from the catalog

• Surface business descriptions and technical documentation

• Apply governance-aware filters

• Return metadata and definitions

RAG-based reasoning does not occur in this mode. Discovery Mode provides clarity without execution.

Discovery Mode does not use RAG. Only Analysis Mode uses RAG.

How askEdgi Finds the Right Context in Analysis Mode

The following sequence describes how askEdgi retrieves context and prepares for execution.

Step 1: Determine Workspace Dependency

askEdgi evaluates whether the request requires existing workspace data.

Workspace data is required when the request:

• Reads existing tables

• Computes metrics from data

• References workspace objects

• Validates schemas

Workspace data is not required when the request:

• Requests an example

• Requests a sample SQL or Python

• Requires logical reasoning without data

• Creates new structures without referencing existing data

This separation improves clarity and efficiency.

Step 2: Evaluate Existing Workspace Context

askEdgi checks whether sufficient context already exists within the Workspace.

If sufficient context exists, search expansion does not occur.

Step 3: Enrich Business Understanding

When additional clarity is required, askEdgi retrieves:

• Glossary definitions

• Asset descriptions

• Metadata context

This ensures the correct interpretation of business intent.

Step 4: Suggest Relevant Assets

When necessary, askEdgi identifies additional datasets aligned with business intent.

Only governed and relevant assets are considered.

Step 5: Validate Dataset Compatibility

Before execution, askEdgi confirms:

• Required attributes exist

• Datasets are structurally connected

• Necessary relationships are available

• Required elements are complete

If validation fails, execution does not proceed.

askEdgi stops and requests clarification instead of executing with incomplete data.

Step 6: Execute Analysis

Execution occurs only after:

• Context is sufficient

• Relationships are confirmed

• Required data elements exist

Execution is intentional and validated.

How askEdgi Handles Missing or Incomplete Information

askEdgi stops intentionally to ensure accurate and trustworthy results.

askEdgi stops when:

• Required data is missing

• Structural compatibility cannot be confirmed

• Business context is unclear

• Confidence is insufficient

askEdgi does not:

• Guess

• Partially execute

• Assume schema

This results in predictable and trustworthy outcomes.

RAG Trust in Analysis Mode

The RAG framework in askEdgi relies on controlled enterprise grounding.

RAG uses the following information sources:

• Curated business descriptions

• Technical documentation

• Structured metadata

• Relationship context between assets

• Lineage information

• Contextual data statistics, such as sample data characteristics

• Top 50 values

Contextual data statistics and top 50 values improve relevance and interpretation. Governed metadata and business definitions remain the primary reference.

askEdgi enforces the following controls:

• Respect governance and access rules

• Validate structural compatibility before dataset combination

• Confirm required attributes before execution

• Stop when information remains incomplete

• Maintain clear separation between discovery and execution

This layered grounding ensures business-aligned, structurally valid, and explainable responses.

RAG Limitations

Certain behaviors remain intentionally restricted to maintain trust.

askEdgi avoids:

• Guessing or fabricating answers

• Ignoring governance controls

• Combining unrelated datasets

• Excessive expansion across the data ecosystem

• Execution with incomplete schema validation

Restraint remains a core system principle.

Business Impact

Organizations gain the following benefits:

• Faster and safer data discovery

• Reduced dependency on technical teams

• Fewer incorrect dataset combinations

• Strong structural validation before analysis

• Higher trust in analytics and reporting

• Streamlined workflows without mode confusion

• Better alignment between business and data teams

askEdgi serves as a reliable entry point to enterprise data knowledge and trusted analysis.

Summary

RAG forms the foundation that makes askEdgi:

• Context-aware instead of generic

• Relationship-aware instead of isolated

• Schema-aware instead of assumptive

• Dependency-aware instead of speculative

• Trusted instead of uncertain

• Business aligned instead of technically driven

Clear separation between Analysis Mode and Discovery Mode with structured validation before execution ensures intentional, explainable, and trustworthy interactions.

Code Explanation Panel in AskEdgi

The Code Explanation Panel is a new feature that provides natural language summaries of SQL and Python code used to generate AskEdgi results. This feature improves accessibility for non-technical users and increases transparency in result generation.

Accessing the Code Explanation Panel

• Open the Code View for a query or analysis result.

• Click on the Explanation tab, positioned next to the existing Code and Copy options.

• AskEdgi generates a concise, human-readable description of the code logic automatically.

Functionality

• SQL Example: “This query retrieves the total sales for each region in 2023 and ranks them by revenue.”

• Python Example: “This script converts unstructured balance sheet text into a structured DataFrame for two fiscal years.”

• Explanations are streamlined and accurate, reflecting the code logic.

• Users can toggle Show More / Show Less to expand or collapse longer explanations.

• The explanation auto-refreshes whenever the code changes or is re-run.

Performance & UX

• A loading indicator is shown while the explanation is being generated.

• Explanations are cached for the session to improve response time on repeated views.

Error Handling

• If the explanation cannot be generated (e.g., API error, timeout, unsupported code format), the following message is displayed:

• “Explanation could not be generated. Please try again or refresh.”

• For large or multi-step Python scripts, explanations are summarized in chunks (e.g., function-level or step-wise).

• Users can optionally view a Detailed Explanation for step-wise logic.

Intelligent Query Source Detection in askEdgi

askEdgi supports Intelligent Query Source Detection, enabling automatic optimization of where a query is executed. Instead of requiring all data to be ingested into the workspace, the system can determine whether a query should run within the workspace engine or be executed directly on the original data source, such as a database or data warehouse.

This capability improves performance, reduces unnecessary data movement, and supports efficient analysis for large or real-time datasets.

Why Intelligent Source Detection Matters

Previously, all datasets needed to be fully ingested into the workspace before analysis could begin. This approach could be inefficient when working with:

• Large datasets

• Live enterprise databases

• Real-time or frequently updated data

• Data warehouses such as Snowflake

With Intelligent Source Detection, askEdgi removes this limitation by dynamically selecting the most appropriate execution environment.

How Intelligent Query Execution Works

When a user submits a query or analytical request, askEdgi automatically evaluates:

• Where the relevant data resides

• Whether execution is more efficient in the workspace or at the source

• Performance, scale, and execution feasibility

Based on this evaluation, askEdgi chooses one of the following execution paths:

• Workspace Execution

  • Queries run inside the askEdgi workspace engine when data is already ingested or best suited for in-workspace processing.

• Source Execution

  • Queries are pushed directly to the original source system, such as a data warehouse, when execution outside the workspace is more efficient.

This ensures faster response times, reduced resource consumption, and improved scalability.

Live Source Query Mode

Live Source Query Mode allows AskEdgi to execute SQL queries directly on supported source systems instead of ingesting data into DuckDB. This enables real-time analytics, minimizes data duplication, and supports environments where data movement is restricted.

When enabled, Live Source becomes the default data querying mode, and all newly added tables are placed under the Live Source section for direct execution.

Connector Configuration - Live Source Checkbox

A Live Source checkbox is available in the AskEdgi settings for supported connectors (Ex: Snowflake).

• Cached Mode (Default)

  • Tables are ingested into DuckDB

  • Full AI enrichment, transformations, recipes, and cross-source joins supported

• Live Query Mode

  • SQL executes directly on the source system

  • No data is copied into DuckDB

  • AI enrichment, transformations, and cross-source joins are disabled

  • Only tables from the same live connector can be queried together

Live Connections in the Workspace

When Live Query Mode is enabled:

• A Live Connections section appears in the workspace

• Tables added from the source catalog display a Live indicator

• No ingestion into DuckDB occurs

• Live tables remain queryable directly on the source

Pinning Rules (Execution)

Rules

Table Type	Pin Allowed
Cached (Imported) Table	✅ Allowed
Live Table	❌ Not Allowed

Live Table Pin Attempt Behavior

A blocking popup is shown:

• Title: Live Table Execution Not Supported

• Message:

  • This table is queried directly from the source system and cannot be pinned for execution. To analyze this data using AskEdgi features, move the table to Imported Data.

• Actions:

  • Move to Imported Data

  • Cancel

Hybrid Execution Blocking (Live + Cached)

If a query references both Live and Cached tables, execution is blocked before SQL generation.

• System Message (Chat)

  • ⚠️ Mixed Data Sources Detected AskEdgi cannot analyze Live and Imported data together. To continue, move the Live table into Imported Data so both tables run in the same engine.

• CTA: Move Live Table to Imported Data

Move-to-Cache Workflow

Users may explicitly move a Live table into Cached mode.

Flow

1. User confirms move

2. Table is removed from Live Source

3. Table is ingested into DuckDB

4. Progress indicator shown

5. On success — pin becomes available

6. User is prompted to rerun query

AI & Feature Limitations for Live Tables

Live tables do not support:

• AI enrichment

• Transformations

• Calculated columns

• DDL / DML operations

• Cross-connector joins

• Hybrid execution

Disabled features display tooltips explaining the limitation.

SQL Execution Routing Logic

Scenario	Execution Engine
All tables cached	DuckDB
All tables live (same connector)	Source System
Mixed Live + Imported	❌ Blocked

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