Smart Analysis & Automation
This article explains the advanced capabilities available in askEdgi for data analysis and collaboration. It describes how Retrieval-Augmented Generation (RAG) delivers business-aware responses, how the Natural Language Editor helps users understand and modify analysis logic, how users can reply to Question Wall notifications directly from supported channels, and how Intelligent Query Source Detection selects the most efficient location to execute queries.
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is the technology that powers askEdgi's ability to provide accurate and business-aware answers by first retrieving relevant enterprise information and then generating responses based on that information. Instead of relying on general AI knowledge or assumptions, askEdgi uses trusted organizational context such as business glossary definitions, metadata, lineage, governance information, data relationships, documentation, and data quality details to understand questions and deliver meaningful answers. This approach helps users find the right data assets, understand business terms, validate relationships between datasets, and receive trustworthy insights that align with the organization's data governance standards and business definitions.
For more detailed information, please refer to Retrieval-Augmented Generation (RAG) User Guide.
Natural Language Editor
The Natural Language Editor enables users to understand, modify, and execute analysis logic without requiring SQL or Python expertise. It provides both the generated code and a business-friendly explanation of the logic used to produce the results.
This capability allows the users to refine the analysis using natural language by editing the Data Flow Logic, while also being able to manually change the code. Changes can only be applied to the current analysis result. Requests that are unrelated to the analysis or cannot be interpreted are rejected with an appropriate error message.
Accessing the Natural Language Editor
After an analysis is generated, users can open the Data Flow Logic by selecting View Code/Logic from the analysis result.
The Code and Data Flow Logic views are displayed side by side, allowing users to view the technical logic and business explanation simultaneously.
Code on the left side, showing the generated SQL or Python.
Data Flow Logic on the right side, showing a step-by-step explanation of the analysis in natural language.
Both views represent the same analysis and remain synchronized throughout the editing process.
Code
The Code section displays the SQL or Python generated by askEdgi.
Users can:
Review, Edit and Rerun the generated code when required.
Execute the updated code and Save the result to update the analysis on the updated analysis.
When code changes are executed:
The analysis results are refreshed.
The previous output is completely replaced with the newly generated output.
The Data Flow Logic is automatically updated to reflect the revised code.
Data Flow Logic
To help users understand the code, the editor provides "data flow logic" which breaks the code down into clear, step-by-step explanations in natural language.
Describes how the analysis is performed, including data selection, filtering, transformations, calculations, and result generation.
Helps business users understand the analysis without reading the underlying code.
Example:
SELECT region,
SUM(sales_amount)
FROM sales
GROUP BY region;
Users see:
Read sales records.
Group records by region.
Calculate total sales for each region.
Return the summarized results.
Users can modify the analysis by updating the Data Flow Logic using natural language. Select a specific part of the logic and enter the required change. askEdgi automatically updates the analysis and regenerates the corresponding SQL or Python code.
Examples:
Change the firstname to lastname by clicking on it.
Add or modify a filter for active customers only.
Group results by department instead of region.
Change the sorting order.
Update a numerical limit.
When the changes are submitted, askEdgi interprets the request and automatically regenerates the corresponding SQL or Python code.
Natural Language Editing
Users can modify the analysis by updating the Data Flow Logic directly.
Common updates include:
Changing filter conditions
Updating calculation criteria
Modifying sorting behavior
Adjusting limits and thresholds
Adding or removing analysis conditions
When the logic is updated:
askEdgi interprets the requested change.
The corresponding SQL or Python code is regenerated.
The updated code is executed.
Analysis results are refreshed.
Data Flow Logic is regenerated to reflect the latest analysis.
Reset Logic
Reset Logic restores the original analysis logic and removes unsaved changes.
When Reset Logic is selected:
The original Data Flow Logic is restored.
The original generated code is restored.
The analysis returns to its initial state.
Validation and Error Handling
If a requested change cannot be applied because it is invalid, unsupported, or unrelated to the analysis, execution does not proceed.
askEdgi displays a validation message explaining why the change could not be applied, allowing the logic to be corrected before re-execution.
If the code is changed manually, it will automatically change the Data Flow Logic to match the changed code.
The user can make multiple changes in the Data Flow Logic at once and execute the logic.
Question Wall – Reply from Notifications
askEdgi supports replying to Question Wall conversations directly from notifications. This allows users to respond to questions without navigating back to the application, improving response time and reducing context switching.
This capability applies to notifications triggered from Question Wall activities and ensures that responses submitted externally are correctly posted back to the corresponding conversation.
Supported Notification Events
Reply capability is available for the following Question Wall events:
Question Assigned
New Reply Received
These events continue to follow the existing notification triggers and delivery behavior.
Supported Channels
Reply to notifications is supported across external notification channels used by the platform.
Users can view the notification and respond directly from the same channel without opening askEdgi.
Reply Integration
Users can submit a reply directly from the notification
The reply is posted to the same Question Wall conversation in the application
The response is treated as a standard reply within the thread
No separate workflow or approval is required
This ensures continuity of discussion regardless of where the response is submitted.
Context in Notifications
Notification content remains aligned with the triggering event.
For New Reply Received notifications:
The notification includes the most recent 2–3 replies for context
Each reply includes:
Author
Timestamp
Plain-text content (truncated if required)
This provides sufficient context for users to understand the conversation before responding.
Scope of Impact
No change to existing notification triggers
No change to notification templates or event definitions
Replies submitted externally are fully integrated into the Question Wall thread
The feature does not modify:
Question creation flow
Assignment logic
Notification audience 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
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
User confirms move
Table is removed from Live Source
Table is ingested into DuckDB
Progress indicator shown
On success — pin becomes available
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
All tables cached
DuckDB
All tables live (same connector)
Source System
Mixed Live + Imported
❌ Blocked
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