The Home page is the primary landing screen after a successful login, presenting system performance metrics, dataset tracking, automatic domain detection, and direct shortcut tiles to every module in GDLytics.

Upon logging in, a temporary popup banner appears: "👋 Welcome Back! Your workspace is ready." The message automatically dismisses after a few seconds without requiring manual clicks.

• System Health: Live resource monitor displaying RAM percentage, CPU load percentage, and the active session duration (Active: HH:MM:SS).
• Global Analytics (Total Cleaned Datasets): A lifetime cumulative counter tracking how many datasets have been cleaned using the application. Starts at 0 and increments automatically.
• Current File & Auto Domain Detection: Displays the currently loaded dataset filename (or 'Waiting..' if none). It automatically analyzes the schema and columns to detect the business domain (e.g., E-commerce, Supply Chain, Healthcare, etc.).

• Upload Data: Import new CSV or Excel files.
• View Clean Data: Inspect raw or processed table rows and columns.
• Auto Clean: Run automated pipelines for formatting and cleaning.
• Visual Insights: Access the dynamic dashboard and charts.
• AI Assistant: Launch the offline copilot for queries.
• View Report: Generate structured AI summaries and PDF briefings.
• Settings: Update credentials and recovery email.
• User Guide: Open the documentation.

You can import your dataset using two simple methods:

Method 1: Using the "Browse File" Button

1. Navigate to the Upload Data tab from the left sidebar.
2. Click the green Browse File button located below the detected domain section.
3. A file explorer window will open. Supported formats include .csv, .xlsx, and .xls.
4. Select your dataset file and click Open. It will load automatically.

Method 2: Drag and Drop

1. Locate your dataset file on your computer.
2. Click and hold the file, then drag it directly into the "Drag and Drop Data File Here" area at the top of the screen.
3. Release the mouse button to drop the file and import it instantly.

After importing, Analytics Pro automatically analyzes your columns and assigns a Detected Domain (e.g., E-commerce & Retail Sales). The entire analytics engine, cleaning rules, and automated insights depend strictly on this domain.

How to Manually Change the Domain:

1. Click the Edit button located in the Detected Domain panel.
2. Select the correct business domain from the dropdown menu.
3. Click the green OK button to confirm your changes.

The Clear Data feature allows you to completely reset the application by removing the currently loaded dataset. It clears all imported data, summary statistics, column health metrics, and previews, resetting the screen to "Waiting for data...".

How to Use:

1. Click the red Clear Data button located below the Detected Domain panel.
2. A confirmation prompt will ask: "Are you sure you want to clear the current data?"
3. Click Yes to wipe the session data, or No to keep it intact.

The Join feature allows you to merge related data from an external file into your currently loaded dataset using a common matching key (Primary/Foreign Key).

Step 1: Select File to Join

• Click the purple Join button below the Detected Domain card.
• In the file dialog, choose the dataset file you wish to merge (supported formats: .csv, .xlsx, .xls) and click Open.

Step 2: Key Validation

• The system automatically verifies if there is a common matching key column (e.g., ProductID) between your main dataset and the selected file.
• If no matching column is found, a "No Match Found" warning appears, and the process will abort until a valid file is selected.

Step 3: Configure Join Settings

• Select Position (Insert After): Choose which existing column the newly joined columns should be placed immediately next to.
• Select Columns to Add: Pick the specific columns you want to import by checking/unchecking the boxes.
• Click Apply Join to proceed.

Step 4: Handling Column Conflicts

• If any column in the joining file shares the same name with an existing column, a Column Conflict dialog will prompt you.
• Click Yes to overwrite the existing column, or No to keep the original and add the new data as duplicate/renamed columns.

The Append feature allows you to add new rows from an external dataset directly to the bottom of your currently loaded data table.

Step 1: Select File to Append

• Click the orange + Append button located below the Detected Domain card.
• Choose the dataset file containing the new rows (.csv, .xlsx, .xls) and click Open.

Step 2: Schema Validation

• The system validates the incoming file's schema against the main dataset. All column headers in both files must match exactly in name and count.
• If there is any discrepancy, a Column Mismatch alert appears, and the append process is blocked.

Step 3: Confirm Append

• If the schema matches successfully, a confirmation dialog will display. Click Yes to append the new rows to the main table, or No to cancel.

The Manual Tool gives you full interactive control to clean, transform, format, and structure individual columns across three main screens: Upload Data, View Clean Data, and Auto Clean. You can access it via two methods:

Method 1: Manual Tool Button

• Click the dark blue Manual Tool button located at the top-right corner of the data preview table.
• This opens the comprehensive panel featuring operations like Text Operations, Missing Values, Add & Split, Rounding & Rename, Rearrange, and a Live Data Preview.

Method 2: Right-Click Context Menu (Quick Shortcut)

• Right-click directly on any Column Header within the data table (just like in Microsoft Excel).
• A quick-access context menu opens instantly with frequent actions such as Add, Rename, Delete, Filter, and Rearrange.

The Upload Data screen serves as the initial ingestion dashboard where you can inspect, validate, and preview your raw dataset immediately after importing.

• Ingestion Status & Metrics: Displays the Loaded File Badge, Total Rows, Total Columns, and the count of Missing Values with a health indicator.
• Detected Domain Classification: AI engine automatically scans and classifies the industry/business category of your dataset. You can manually edit this using the Domain Editor.
• Raw Column Health Status: A table listing every column name, its Detected Format (e.g., Text, Numeric, Date), and individual health badges (e.g., ✅ Perfect).
• Raw Data Preview: An interactive spreadsheet preview of the first 100 records to inspect header names and initial formatting before cleaning.

The View Clean Data screen provides a comprehensive health audit, schema inspection, and live preview of your cleaned dataset before moving into advanced analytics or export.

High-Level Summary Metrics (Top KPI Cards)

• Cleaned Rows: Displays the total volume of valid records available after dropping blanks and duplicates (e.g., 80,000 rows).
• Cleaned Columns: Shows the total number of active columns retained in your structured schema.
• Missing Values: Highlights the total count of null/blank cells remaining in the entire dataset with a quick visual indicator (e.g., 0 ✅).

Cleaned Column Health Status (Schema Audit Table)

• Column Name: Itemizes every column present in your processed dataset.
• Clean Format: Displays the standardized data type assigned to each column (e.g., Text/String, Integer, Decimal, Date).
• Status: Provides an individual validation badge (e.g., ✅ Perfect) confirming that the data conforms properly to its assigned format without inconsistencies.

Cleaned Data Preview (Top 100 Rows Table)

• Displays an interactive spreadsheet view of the top 100 rows of your fully transformed data. Lets you visually verify standardized column headers, clean text casing, unified YYYY-MM-DD date structures, numerical alignments, and extracted geographical fields.

The Fill date feature allows you to automatically fill missing or blank date entries using the preceding valid dates (forward fill) in your dataset.

Step 1: Accessing the Tool

• On the View Clean Data screen, click the purple Fill date button located above the Cleaned Data Preview table to open the modal.

Step 2: Selecting the Target Date Column

• The dropdown menu automatically filters and displays only the columns identified as Date format. Select your desired date column (e.g., Order_Date) and click OK.

Step 3: Confirmation & Execution

• A prompt will ask: "Are you sure you want to fill blank dates in '[Column_Name]'?"
• Click Yes to proceed. The engine will instantly propagate the previous valid date downward to fill any subsequent blank date cells.

The Manual Tool gives you full interactive control to clean, transform, format, and structure individual columns.

Method 1: Manual Tool Button

• Click the dark blue Manual Tool button located at the top-right corner of the data preview table to open the comprehensive cleaning panel.

Method 2: Right-Click Context Menu (Quick Shortcut)

• Right-click directly on any Column Header within the data table (just like in Microsoft Excel) to open a quick-access menu for frequent actions like Add Column, Rename, Delete, Filter, Unfilter, and Rearrange.

The Calculation feature allows you to verify mathematical formulas, fix incorrect calculations, and automatically recover missing data directly from the report view.

Step-by-Step Guide:

1. Access the Tool: Click the dark green Check Calculation button at the top-right corner of the Cleaned Data Preview table.
2. Input Your Logic: Start typing your mathematical formula (e.g., Quantity*Unit_Price(1-Discount_%)=Total_Sales). Use the Smart Auto-Complete dropdown to insert exact column names quickly.
3. Smart Data Validation: Click OK. The system tests your formula against sample data. If accuracy falls below 80%, the logic is rejected to protect your data.
4. Review the Action Table:
   • Output Section: Tick checkboxes to fix mathematically wrong or completely blank calculations in your target column.
   • Input Section: For missing inputs, choose Method 1 (tick 'Blank' to let the engine mathematically reverse-calculate the missing number) OR Method 2 (manually type a custom number like 0 into the box to fill all blanks).
5. Execute Actions: Click the purple Execute Actions button.
6. Handle Alerts (If Applicable): Allow the engine to convert illogical negative numbers to positive if prompted. If multiple inputs are missing in a single row, choose to either delete those incomplete rows (Yes) or fill missing blanks with 0 (No).
7. Success: A "Data updated successfully!" message will confirm that your main dataset has been synchronized with the corrected math.

The Primary cleaning button executes an automated baseline preparation pipeline to standardize, clean, and structure your imported dataset.

Key Features:

• Garbage & Redundancy Removal: Automatically identifies and removes completely empty rows/columns, exact duplicate rows, and redundant duplicate columns.
• Header Normalization: Matches and renames column headers against domain-specific naming standards. Unmatched headers are flagged for optional review in the Mapping tool.
• Automatic Data Type Inference: Cleans currency symbols/commas to convert values to standard integers or decimals, standardizes percentage formats, and accurately casts booleans.
• Intelligent Date Standardization: Detects and converts mixed date formats into standard YYYY-MM-DD, separates combined date-time entries, and normalizes two-digit years.
• Sorting & Precision: Automatically sorts the dataset chronologically based on date columns and rounds decimal columns to a 2-decimal-place precision.
• Unlocking Advanced Actions: Successfully running Primary cleaning unlocks all remaining pipeline buttons (Mapping, Manual Tools, Auto, Calculation, etc.).

The Mapping tool allows you to manually review and standardize column headers that were not automatically matched during Primary cleaning.

Steps to Use:

1. Click the dark navy-blue Mapping button next to the Primary button.
2. The Mapping Engine modal displays unmatched columns with their original name and a live sample preview.
3. Renaming Options: Use the dropdown to select a predefined standard name, use the interactive search, or type a custom name directly into the field.
4. Click Apply Mapping to save changes to the main dataset. Remaining unmapped columns will stay in the queue for future edits.

The Auto Clean feature thoroughly scans, corrects, and standardizes your dataset through a multi-phase automated pipeline. Note: Primary cleaning must be run first.

Phase 1: Geographical Data Correction & Mapping

• Smart Bypass & Country Validation: If a 'City', 'Town', or 'Village' column is missing, the phase is bypassed. If present, it prompts for country confirmation to generate 'State' and 'Region' columns.
• Noise Removal & Current Name Update: Removes special characters/spaces and updates historical city names to their current official names (e.g., Bombay to Mumbai).
• Cross-Mapping Correction: Fixes mismatched geographical pairs automatically.
• Unmatched City Isolation: Unrecognized cities are safely isolated into an 'Unmatched List' for manual mapping later.

Phase 2: Dynamic Text Cleaning & Auto-Healing

• Sensitive Data Protection: Locks identifier columns (IDs, Emails, etc.) to prevent accidental alteration.
• Auto-Healing: Scans and corrects spelling mistakes by applying the most common correct spelling found across the dataset.
• Smart Formatting: Cleans email formats, removes currency symbols/text from monetary fields, and applies intelligent Title Case formatting.

Phase 3: Smart Missing Value & Entity Imputation

• Gender Normalization: Standardizes messy short codes (like 'm', 'f') into proper labels (Male and Female).
• Blank Filling: Safely fills empty records in the gender column with 'Unknown' to ensure completeness.

The Calculation feature allows you to verify mathematical formulas, fix incorrect calculations, and automatically recover missing data.

Step-by-Step Guide:

1. Click the red Calculation button. Use the "View Column List" button if you need to see available numerical columns.
2. Input Your Logic: Type your mathematical formula (e.g., Quantity*Unit_Price(1-Discount_%)=Total_Sales) utilizing the Smart Auto-Complete dropdown.
3. Click OK for Smart Data Validation. If accuracy is below 80%, the engine will reject it to protect your data.
4. Review Action Table:
   • Output Section: Tick checkboxes to fix mathematically wrong or blank output rows.
   • Input Section: Choose Method 1 (tick 'Blank' for Auto Reverse Calculation) OR Method 2 (manually type a custom number to fill blanks).
5. Click Execute Actions. You may receive prompts to convert negative numbers or handle multiple blanks (delete rows vs fill with 0).
6. Click OK on the success message to synchronize updates to your main dataset.

The Formatting tool provides full manual control over your dataset's column data types and allows standardization of percentage formats.

How to Use:

1. Click the olive-green Formatting button to open the window listing all columns and their detected formats.
2. Change any column's format by clicking the dropdown under "Change Format To" (Text, Integer, Decimal, Date, Datetime, Boolean).
3. Click Convert Save to apply changes, or Cancel / Back to discard them.

Convert to Standard %: Click the dark blue button at the bottom, select a whole-number percentage column (e.g., 10%), and click OK to convert it to a standard decimal value (e.g., 0.10).

The Manual Tool gives you interactive control to clean, transform, and structure columns. Access it via:

Method 1: Manual Tools Button

• Click the pink Manual Tools button in the top action bar (3rd button from the left).
• This opens the comprehensive panel with operations like Text Operations, Missing Values, Add & Split, Rounding & Rename, Rearrange, Multiple Replace, and a Live Data Preview.

Method 2: Right-Click Context Menu (Quick Shortcut)

• Right-click directly on any Column Header within the data table.
• A quick-access context menu opens instantly with frequent actions such as Add Column, Rename, Delete, Filter, Unfilter, and Rearrange.

To save and download your fully processed dataset:

1. Click the purple Export Data button on the Data Processing Engine toolbar.
2. In the "Save Clean Data" window, choose your File Location and File Name (default is Cleaned_Data).
3. Select your Save as Type (.csv or .xlsx).
4. Click Save to export, and a confirmation message will appear.

The Engine Logs console displays a real-time stream of all events and background actions happening across GDLytics, including:

• System & Ingestion Status: File loading confirmations and database alerts.
• Action Unlocks: Notifications when tools become active (e.g., "Action Unlocked: Manual button is now active!").
• Data Cleaning Updates: Column renaming, formatting, sorting, and decimal rounding confirmations.
• Auto-Cleaning Progress: Location processing (detected countries/cities), typo corrections, title casing, and missing-value fills.
• Calculation Alerts: Pass/fail results of mathematical logic and synchronization statuses.
• Dashboard Status: Confirmation when visual dashboards and reports are fully generated.

To transition your cleaned dataset directly into the interactive visualization workspace:

1. Click the teal Analyze Data button on the toolbar (or select Visual Insights from the left sidebar) to jump to the dashboard.
2. Click the dark blue Auto Insights button at the top-right toolbar.
3. The engine instantly processes the data to generate KPI Summary Cards (Total Records, Sales, etc.), Interactive Charts (Bar, Histogram, Pie, Line), and Domain-Specific Filters (metric, year, category) dynamically.

The Visual Insights screen provides an interactive business intelligence workspace to explore aggregated KPIs, growth benchmarks (YoY / MoM), filter dimensions, and examine multi-format analytical charts.

Key Components:

• Domain Header & Theme Controls: Toggle dashboard appearance presets (e.g., Light Pro) and view the identified industry context (e.g., E-commerce & Retail Sales).
• Multi-Dimensional Filters Bar: Dynamic dropdown filters across the top to slice data by Metric (e.g., Sales), Time Dimensions (Year/Month), and Categorical Dimensions (e.g., Category, Product).
• KPI Summary Cards: Displays Total Records, Total Sales, Avg Total Sales, Unique Customers, and Unique Order IDs.
• Dynamic Growth Badges: Shows Year-over-Year (YoY) growth percentages when a specific year is selected, and dynamically switches to Month-over-Month (MoM) when a specific month is selected.
• Interactive 4-Chart Grid: Features a Categorical Bar Chart, Data Distribution Histogram, Proportional Pie Chart, and Time-Series Line Chart.

The Auto Insights tool enables one-click automated dashboard generation from your processed data.

1. Access the workspace by clicking the teal Analyze Data button on the Auto Clean page, or select Visual Insights from the left sidebar. The initial page will open with blank placeholders.
2. Click the dark blue Auto Insights button located at the top-right toolbar.
3. The engine instantly scans your cleaned dataset, detects dimensions, and automatically builds the full interactive dashboard including KPI Metric Cards, Dynamic Filters, and the Visual Charts Grid.

The Refresh feature instantly synchronizes and re-renders the Visual Insights dashboard whenever structural or numerical changes occur in your underlying data.

When to Use: Use this after adding/splitting columns, renaming categories, updating numerical values, or filtering/removing specific rows.

Execution: Click the green Refresh button on the top-right action toolbar. The engine immediately re-queries the dataset to recalculate all KPI cards, update filter dropdowns, and re-plot all 4 visualization charts live.

You can customize the appearance using curated presets or a complete manual color palette.

• Preset Themes: Click the theme dropdown at the top-left to choose from 10 professional presets (e.g., Light Pro, Dark Night, Cyberpunk).
• Custom Theme Builder: Select "Custom" from the dropdown. You can click color blocks to customize the Background, Card/Box, Text, and Border colors.
• Color Picker Guide: Use the basic swatches, the 2D gradient slider, or the eyedropper tool to pick screen colors. You can also manually enter Hex (e.g., #1eff38), RGB, or HSV values. Save your shades to custom slots and click "Apply Custom Theme".

The Export PDF feature allows you to capture and save the complete dashboard as a publication-ready document.

1. Click the orange Export PDF button located at the top-right corner.
2. In the Save dialog window, select your destination folder and enter a custom file name (defaults to Dashboard_Report).
3. Click Save to generate your high-resolution visual report.

Click the purple Manual Tool button on the toolbar to open the Configuration modal.

• KPI Customization Panel: Select which card to configure (Card 1–5). Assign a custom title, select the source column, and apply category filters (Select All or a specific value). For Text columns, calculations are restricted to Count/Unique/Percentage. For Numeric columns, choose Sum, Avg, Min, Max, etc. Select your display unit (Auto, K, M).
• Slicer Customization Panel: While the Metric, Year, and Month slicers are fixed, you can manually assign dataset columns to Slicer 1 and Slicer 2, or leave them on Auto.
• Click Apply to update the live dashboard or Back/✕ to exit.

Chart 1 displays category-wise metric comparisons (e.g., Total Sales by City).

• Dropdown Controls: Select a categorical column for the X-Axis, a numeric metric for the Y-Axis, and choose the Chart Type (Vertical Bar or Horizontal Bar).
• Manual Lock Mode: Manually selecting columns locks your choices. The chart will update filtered values based on global slicers but will not automatically switch axis columns.
• Dynamic Reset: To return to dynamic auto-adaptation, Double-Click anywhere on the empty/white canvas area inside the chart. Your chosen Chart Type will remain preserved.

Chart 2 analyzes numerical distribution, frequency, and value spread.

• Dropdown Controls: Select the numerical metric column and choose the Chart Type (Histogram for frequency distribution, or Scatter for plotting individual data points and outliers).
• Manual Lock Mode: Manual selections lock the chart to your chosen numeric column, ignoring global slicer column switches but updating filtered data points.
• Dynamic Reset: Double-Click the empty chart canvas to clear manual locks and return to automatic data adaptation while keeping your Chart Type intact.

Chart 3 visualizes market share and percentage breakdown across categorical segments.

• Dropdown Controls: Select a categorical field/column (e.g., Payment_Mode) and choose the Chart Type (Pie for solid slices, Donut for hollow ring, or Treemap for nested rectangular tiles).
• Manual Lock Mode: Selecting a column locks it. The chart remains pinned to this category during global slicer interactions, only updating the percentage distributions.
• Dynamic Reset: Double-Click the empty chart canvas to restore automatic categorical column tracking. Your visual style (Pie/Donut/Treemap) will be saved.

Chart 4 plots performance trajectories and seasonal trends across dates.

• Dropdown Controls: Select a Date/Timeline column for the axis (e.g., Order_Date) and choose the Chart Type (Line for connected points/markers, or Area for color-filled volume).
• Manual Lock Mode: Manually choosing a date column pins the chart. It will update filtered values from global slicers but won't switch its timeline axis.
• Dynamic Reset: Double-Click the empty chart canvas to resume dynamic auto-adaptation to the active dataset's primary date dimension.

The AI Assistant screen provides a completely offline, built-in AI copilot designed to help you navigate GDLytics functionalities and run natural language queries directly on your imported dataset without needing an internet connection.

Prompting Best Practice (To-the-Point Queries)

• Keep queries concise, clear, and direct in plain English.
• Overly complex or excessively long paragraphs may cause the offline NLP engine to return valid calculations that do not fully match your exact intent. Sticking to direct phrases ensures maximum accuracy.

App Knowledge & Feature Guidance

• The assistant explains how features, workflows, and configuration tools inside the application work.
• It provides instant step-by-step guidance on cleaning pipelines, formatting options, and dashboard customizations.

Type plain English queries into the prompt box and hit Enter to receive instant computational answers:

• Direct Aggregations: Run targeted multi-variable queries (e.g., "Total Sales by City" or "City wise Total Quantity by Electronic Category").
• Rankings & Metadata Inspection: Extract top performers or pull dimension lists (e.g., "Top 3 product by Total sales" or "Show product list").
• Statistical Summaries: Query averages, counts, or ratios (e.g., "Average discount").
• Date-Range Slicing: Calculate metrics between specific time intervals (e.g., "Total sales from 17-03-2026 to 21-03-2026").
• Conditional Multi-Criteria Filters: Filter ranges using logical operators (e.g., "Total sales by product where unit_price between 500 and 1500").
• Growth Computations (MoM / YoY): Analyze periodic changes dynamically (e.g., "MoM by total sales for February").
• Predictive & Advanced Analytics: Generate forward-looking sales forecasts (e.g., "Predict next 3 month total sales") or instantly identify data irregularities (e.g., "Checking Anomaly").

Here is a quick reference guide for how to structure your queries across different categories:

• Basic Aggregations: "Total city", "Count unique Customer id", "Total Quantity by City Name", "Maximum Cost price by Product Name".
• Logical Filtering: "Total Sales where Category Name is Mobile", "Profit where Unit Price < 25000".
• Multi-Criteria Filters: "City wise total sales by category name by product name".
• Date-Range Slicing: "Date wise Total Sales from 2024-01-01 to 2024-01-31", "Total Cost for January 2024".
• Double Range & Conditions: "Total Sales from 2024-01-01 to 2024-01-31 where Unit Price between 20000 and 40000", "Total Quantity where City Name is Chennai and Category Name is TV".
• Rankings & Metadata: "Top 5 Product Name by Total Sales", "Bottom 3 Sales Representative by Profit", "Show details of Category Name".
• Growth: "MoM by Total Sales", "YoY by Profit".
• Predictive & Advanced Analytics: "Predict next 3 months Total Sales", "Predict next 6 months Profit by City Name", "Checking anomaly in Profit", "Checking anomaly in Total Sales where Category Name is TV", "Find duplicate records", "Check duplicate in Order ID".

Yes (Special Case for Numbers as Categories):

If a categorical filter value consists of numbers (such as a status code or ID), you must enclose the value in quotation marks so that the query engine correctly recognizes it.

Examples:

• Total bytes_downloaded where status_code = '503'
• Cost price of product id "120"
• Total Sales of City id "12"

Follow these simple steps to export your AI query results:

1. Click the Export Result button to open the Selective Export popup window.
2. From the list, select the checkbox next to the query results you want to export.
3. Choose your desired format by clicking one of the option buttons: Export to Excel, Export to CSV, or Export to PDF.
4. Select your destination file path, enter your file name, and click OK.

⚠️ Important Export Rules & Format Constraints:

• Table Results: Tabular outputs can only be exported to Excel or CSV. They cannot be exported to PDF.
• Single-Line Results: Direct text or single-line results can only be exported to PDF. They cannot be exported to Excel or CSV.
• No Mixed Formats: You cannot mix single-line results and table results together in a single export format.

The View Report screen serves as an advanced business intelligence and executive decision engine. It automatically transforms your processed dataset into strategic summaries and formal narrative reports with a single click.

Important Pre-requisite: Always ensure that your dataset is fully loaded and properly cleaned through the cleaning pipeline before generating reports. Generating reports on uncleaned or partially processed raw data may lead to missing analysis blocks, incorrect KPI metrics, or flawed business insights.

You must follow a specific sequence; you cannot jump straight to generating the narrative text report without analyzing the data first.

1. Initial Landing State: Displays a blank canvas if no report is generated yet.
2. Step 1: View Result (Generate Summary): Click the dark blue View Result button to trigger deep statistical analysis and display the structured Universal AI Insights summary.
3. Step 2: Generate Report (Executive Narrative): Once results are generated, click the green Generate Report button to transform the computed insights into a formal, multi-section Executive Data Analytics & Business Growth Report.
4. Step 3: Refresh: Click the purple Refresh button if your underlying data has been modified or re-cleaned, ensuring all analytical models re-sync with the latest dataset.

Click the red Export to PDF button located at the bottom-right corner. The export engine dynamically captures whichever view is currently active on your screen:

• If you are viewing the Universal AI Insights (Result Summary), it exports the structured KPI and metrics summary as a clean PDF.
• If you are viewing the AI Automated Executive Report (Narrative Text), it exports the complete formal executive memo with all detailed sections and strategic recommendations as a publication-ready PDF document.

The structured summary includes:

• Revenue & Financial Health: Calculates Total Revenue, Average Order Value (AOV), and 12-month baseline revenue trajectories.
• Driver / Drainer & Pareto Analysis: Identifies top 10% revenue drivers vs lowest drainers and applies 80/20 Pareto distribution rules.
• Inventory & Supply Chain Blueprint: Segregates fast-moving inventory requiring safety stock from slow-moving dead stock needing aggressive liquidation.
• Customer Health & VIP RFM: Tracks repeat customer rates, basket sizes, revenue split, and lists top 10% high-value VIP buyers.
• Returns & Risk Diagnostics: Quantifies lost revenue from returns/cancellations, isolates return hotspots, and evaluates delivery delay impacts.
• Geographical Allocation: Evaluates regional performance to provide clear stock dispatch decisions (e.g., allocate to high-demand hubs vs freeze to low-demand cities).
• Predictive AI Sales Forecast: Provides forward-looking quarter-by-quarter sales forecasts for the upcoming 3 months.

The formal management memo includes:

• Executive Summary: Summarizes high-level revenue health, overall growth direction, and critical sales anomalies.
• Structured Deep-Dives: Formally presents Revenue Drivers, Supply Chain roadmaps, Customer Acquisition flags, Geographic inventory strategies, and Logistics diagnostics.
• Key Recommendations (Action Plan): Delivers clear, prioritized strategic directives covering Customer Acquisition (CAC), Revenue Recovery, Quality Assurance (QA) return audits, and Working Capital liquidation plans.

The Settings page provides account administration and recovery management tools, allowing you to update credentials, configure security recovery emails, and manage access passwords.

Account Credentials & Recovery Configuration:

• Username: Set or update the primary login account username (e.g., admin).
• Password & Confirm Password: Enter and verify your new login security password.
• Gmail ID (Account Recovery Email): Enter your verified recovery Gmail address (e.g., abc@gmail.com).
• Automated Password Reset Email: If you forget your login credentials, the system sends an email to this address containing both your registered Username and a 6-digit OTP code.
• OTP Validity: The received OTP code remains active for 10 minutes to complete a secure verification and password reset.

Password Visibility Controls: Click the eye icon located on the far right of the Password and Confirm Password fields to toggle between masked view (••••) and plaintext display (1234), ensuring accurate input before saving.

Action Controls:

• Save Settings (Green Button): Saves and permanently updates your username, new password, and recovery Gmail ID in the local database.
• Reset (Pink Button): Instantly clears both the Password and Confirm Password input fields, allowing you to re-type fresh credentials without modifying the existing Username or Gmail ID.

The Contact Us tool allows you to easily reach our support team directly from the app. Whether you need to report a bug, request a new feature, or ask a general question, you can send a detailed message and attach screenshots without ever leaving the software.

Important Pre-requisite: Before sending a message, ensure you have saved your email address in the app's ⚙️ Settings menu. Our support team will use this email to reply to your query.

How to Send a Support Request:

1. Step 1: Click on 📩 Contact Us from the left navigation sidebar.
2. Step 2: Click the Issue Type dropdown menu and select the category that best fits your inquiry (e.g., Bug / Error Report, Feature Request, License & Payment, or General Query).
3. Step 3: Fill in the Subject and Description text boxes with the details of your query. Please provide as much detail as possible so our team can assist you effectively.
4. Step 4 (Optional): Click the 📎 Attach File / Screenshot button to include visual evidence or data files.
   • You can attach a maximum of 3 files.
   • If you select a file by mistake, simply click the red ✖ button next to the file's name to remove it.
5. Step 5: Click the blue Send Message 🚀 button at the bottom of the page.
6. Step 6: A success message will appear displaying your unique Ticket ID (e.g., #DAP-1234). Our team will review your request and get back to you shortly!

The Add Column feature inserts a new blank column directly into your dataset. Placement Rule: The system automatically places it directly to the right side (after) the column that was selected or right-clicked.

Method 1: Using the Manual Data Cleaning Laboratory

1. Select the reference column from the top "1. Select Column" dropdown.
2. Under "4. Add & Split", click the Add Column button.
3. An "Add New Column" popup will open. Type the new column name and click OK.

Method 2: Using the Right-Click Context Menu (Shortcut)

1. Right-click directly on any Column Header in the table.
2. Click ➕ Add Column (the first option).
3. Type the desired column name in the popup and click OK.

The Delete Column feature permanently removes an unnecessary column.

Method 1: Using the Manual Data Cleaning Laboratory

1. Select the target column from the dropdown.
2. Under "7. Danger Zone", click the red 🗑️ Delete Entire Column button.
3. Review the Live Data Preview showing a warning.
4. Click the green Apply Change button to permanently delete the column.

Method 2: Using the Right-Click Context Menu (Shortcut)

1. Right-click the target Column Header in the table.
2. Select 🗑️ Delete Column.
3. Click Yes on the Confirm Delete security dialog to instantly delete the column.

Method 1: Batch Rename (Laboratory Panel)

1. Under "5. Rounding & Rename", click the Rename Column button.
2. A Rename Columns matrix window will open. Type your new names under the "New Name" column. Leave fields blank to keep them unchanged.
3. Click Apply Rename and confirm with Yes.

Method 2: Instant Single Rename (Right-Click Shortcut)

1. Right-click the target Column Header in the table.
2. Select ✏️ Rename Column.
3. Edit the text in the popup and click OK to update instantly without secondary confirmation.

The Rearrange Columns feature allows you to reorder positions using a visual drag-and-drop interface.

Method 1: Using the Laboratory Panel

1. Under "6. Rearrange & Suffix & Prefix", click the Rearrange button.
2. Drag and drop the column names to your preferred order.
3. Click Apply Order and confirm with Yes.

Method 2: Using the Right-Click Context Menu

• Right-click any Column Header and select ↔️ Rearrange to open the exact same drag-and-drop popup directly over the table.

This tool converts all letters to capital letters (e.g., Tablet -> TABLET).

• Method 1 (Laboratory): Select column -> Under "2. Text Operations", click Upper Case -> Review Live Preview -> Click Apply Change -> Click Yes to confirm.
• Method 2 (Right-Click Shortcut): Right-click the Column Header -> Select 🔠 Upper Case. The column instantly transforms without a confirmation popup.

This tool converts all characters to small letters (e.g., TABLET -> tablet).

• Method 1 (Laboratory): Select column -> Under "2. Text Operations", click Lower Case -> Review Live Preview -> Click Apply Change -> Click Yes to confirm.
• Method 2 (Right-Click Shortcut): Right-click the Column Header -> Select 🔡 Lower Case to instantly transform the text.

This tool capitalizes the first letter of each word (e.g., SMART WATCH -> Smart Watch).

• Method 1 (Laboratory): Select column -> Under "2. Text Operations", click Title Case -> Review Live Preview -> Click Apply Change -> Click Yes to confirm.
• Method 2 (Right-Click Shortcut): Right-click the Column Header -> Select 🔤 Title Case to apply the formatting instantly.

The Smart Case feature intelligently analyzes your text data, groups values based on their unique capitalization patterns (e.g., UPPERCASE, Title Case, mixedCase), and allows you to apply specific case transformations to individual groups.

💡 Why use Smart Case? (Advanced Capability)
Imagine your dataset has messy variations of the same word, like UPI, upi, and U"pi. Smart Case is advanced enough to detect these as 3 distinct pattern groups. Instead of manually finding and fixing them, you can simply set the action to "Upper Case" for these specific groups, click apply, and instantly standardize every single variation in your entire dataset into a clean, uniform UPI!

Method: Using the Right-Click Context Menu (Header Shortcut)

1. Step 1: Right-click directly on the target text Column Header in the table (e.g., Product_Name). (Note: This feature is only available for text/string columns).
2. Step 2: Select 🪄 Smart Case from the context menu.
3. Step 3: A Smart Case window opens. The tool automatically analyzes the column and organizes the data into distinct categories under the Pattern Group column (e.g., Type 1, Type 2), displaying sample data under Example Values.
4. Step 4: In the Select Action column, click the dropdown menu for a specific pattern group (default is "Current Type"). Select your desired formatting action (e.g., Upper Case, Lower Case, Title Case).
5. Step 5: Check the box in the Apply column next to the specific pattern group you want to transform right now.
6. Step 6: Click the green ✨ Apply button to execute the change. Once applied, that specific pattern group will be processed and removed from the popup, leaving the untreated patterns for you to work on one by one. When finished, click the red ❌ Back button to close the popup.

The Trim Spaces tool removes unnecessary extra blank spaces strictly from the beginning (leading) and end (trailing) of text entries in a selected column, while keeping all middle spaces completely intact (e.g., " Smart Watch " -> "Smart Watch").

• Method 1 (Laboratory): Select column -> Under "2. Text Operations", click Trim Spaces -> Review Live Preview -> Click Apply Change -> Click Yes to confirm.
• Method 2 (Right-Click Shortcut): Right-click the Column Header -> Select ✂️ Trim Spaces. The entire column instantly removes leading and trailing spaces without a confirmation dialog.

The Drop Nulls tool detects and removes entire rows that contain missing or empty (NULL) values within a selected column.

Method 1: Using the Manual Data Cleaning Laboratory

1. Select the target column (e.g., Customer_Name) from the dropdown.
2. Under "3. Missing Values (Nulls)", click the Drop Nulls button.
3. The Live Preview highlights affected rows with a red 🚫 ROW DROPPED indicator.
4. Click Apply Change and confirm with Yes to permanently remove those rows.

Method 2: Using the Right-Click Context Menu (Shortcut)

• Right-click the Column Header -> Select ❌ Drop Nulls -> The Laboratory preview opens with NULL rows marked -> Click Apply Change -> Confirm with Yes.

The Fill Nulls tool replaces missing, empty, or NULL values in a selected column with a custom fallback value (e.g., Unknown, N/A, or 0).

Method 1: Using the Manual Data Cleaning Laboratory

1. Select the target column from the dropdown.
2. Under "3. Missing Values (Nulls)", click the Fill Nulls button.
3. A popup will appear. Type your desired replacement text or number (e.g., Unknown) and click OK.
4. Review the staged replacements in the Live Preview.
5. Click Apply Change and confirm with Yes.

Method 2: Using the Right-Click Context Menu (Shortcut)

• Right-click the Column Header -> Select ✅ Fill Nulls -> The Laboratory opens and prompts for a value -> Enter value and click OK -> Click Apply Change -> Confirm with Yes.

The Rounding tool formats numbers by either converting them to whole integers or rounding them to a specific number of decimal places (1 to 4). Note: If a non-numeric column is selected, an "Invalid Column" warning blocks the operation.

Method 1: Using the Manual Data Cleaning Laboratory

1. Select the numerical column from the dropdown.
2. Under "5. Rounding & Rename", click the Rounding button.
3. Choose Whole Number (rounds to nearest integer) OR Decimal (enter decimal places from 1 to 4).
4. Review calculations in the Live Preview, click Apply Change, and confirm with Yes.

Method 2: Using the Right-Click Context Menu (Shortcut)

• Right-click a numerical Column Header -> Select Rounding -> Type the desired decimal count (e.g., 2) and click OK. The column instantly updates without requiring a live preview.

The Add Suffix tool appends custom text, characters, or symbols to the very end (trailing part) of every value (e.g., ORD100467 -> ORD100467_US).

Method 1: Using the Manual Data Cleaning Laboratory

1. Select the target column from the dropdown.
2. Under "6. Rearrange & Suffix & Prefix", click the Suffix & Prefix button.
3. Click the Add Suffix (After) button.
4. Type your desired text in the dialog and click OK.
5. Review the appended text in the Live Preview, click Apply Change, and confirm with Yes.

Method 2: Using the Right-Click Context Menu (Shortcut)

• Right-click the Column Header -> Select 🅰️ Add Suffix -> The Laboratory opens and prompts for input -> Type suffix and click OK -> Click Apply Change -> Confirm with Yes.

The Add Prefix tool allows you to seamlessly insert custom text, characters, or symbols at the very beginning of every entry (e.g., ORD100467 -> ABORD100467).

Method 1: Using the Manual Data Cleaning Laboratory

1. Select the target column from the dropdown.
2. Under "6. Rearrange & Suffix & Prefix", click the Suffix & Prefix button.
3. Click the Add Prefix (Before) button.
4. Type your desired text (e.g., AB) in the dialog and click OK.
5. Review the updated values in the Live Preview, click Apply Change, and confirm with Yes.

Method 2: Using the Right-Click Context Menu (Shortcut)

• Right-click the Column Header -> Select 🅱️ Add Prefix -> The Laboratory opens and prompts for input -> Type prefix and click OK -> Click Apply Change -> Confirm with Yes.

The Split Column tool divides a single column into multiple distinct columns based on specific characters, character counts, or data types (e.g., splitting ORD100467 into ORD and 100467).

Advanced Split Options:

• By Delimiter: Splits text using Space, Comma, or Custom inputs (at All, First, or Last occurrence).
• By Position: Splits text after a specific character count from a chosen direction (e.g., First from Left).
• By Data Type (Smart Split): Automatically separates letters from numbers without needing a specific delimiter (e.g., Kolkata700001 -> Kolkata and 700001).

Method 1 (Laboratory): Select column -> Under "4. Add & Split", click Merge & Split -> Click Split Column -> Choose tab and configure -> Click Generate Preview -> Review Live Preview -> Click Apply Change -> Confirm with Yes.
Method 2 (Right-Click Shortcut): Right-click Column Header -> Select ✂️ Split Column -> Choose split logic and generate preview -> Click Apply Change and confirm with Yes.

The Merge Column tool allows you to combine data from two separate columns into a single new column, separated by a chosen delimiter (e.g., merging First_Name and Last_Name).

Method 1: Using the Manual Data Cleaning Laboratory

1. Select your primary column (Base Column) from the dropdown.
2. Under "4. Add & Split", click Merge & Split and select Merge Column.
3. Configure the Merge dialog options: Select Column to Merge With, choose a Separator (Delimiter), enter a New Column Name, and optionally check "Delete original columns after merge".
4. Click Generate Preview to check the Live Data Preview.
5. Click Apply Change and confirm with Yes to apply the merged column.

Method 2: Using the Right-Click Context Menu (Shortcut)

• Right-click the target Column Header (sets it as Base Column) -> Select 🔗 Merge Column -> Choose second column, separator, and new name -> Click Generate Preview -> Click Apply Change and confirm with Yes.

The Unique Values tool allows you to quickly scan and review a complete list of all distinct items present within a specific column, along with the total count of those unique entries.

Method 1: Using the Manual Data Cleaning Laboratory

1. Select the target column from the dropdown.
2. Under "8. Advanced Engine", click the purple Unique Column (Multiple Replace) button.
3. View the complete list under Unique Values (Original), check the Total Unique count at the top right, and use the search bar (type [BLANK] for empty cells).
4. Click the dark grey Close button when finished inspecting.

Method 2: Using the Right-Click Context Menu (Shortcut)

• Right-click the target Column Header -> Select ✨ Unique Values -> Inspect distinct values and total count -> Click Close to return.

The Multiple Replace tool allows you to batch-edit or standardize multiple distinct values within a column simultaneously.

Method 1: Using the Manual Data Cleaning Laboratory

1. Select the target column from the dropdown.
2. Under "8. Advanced Engine", click the purple Unique Column (Multiple Replace) button, then click the orange Replace button at the bottom left.
3. Use the Search values bar at the top to filter long lists instantly.
4. Input replacement values into the "Replace With" column using Manual Typing, Left-to-Right Drag & Drop (drag from left column to right), or Right-to-Right Drag & Drop (copy between cells on the right).
5. Click the green OK button and confirm with Yes in the popup dialog to permanently apply changes.

Method 2: Using the Right-Click Context Menu (Shortcut)

• Right-click the Column Header -> Select ✏️ Multiple Replace -> Enter replacement values using typing or drag-and-drop -> Click OK and confirm with Yes.

The Mapping tool allows you to cross-reference and map the unique values of your selected base column to the unique values of an entirely different column within your dataset.

Method 1: Using the Manual Data Cleaning Laboratory

1. Select your primary base column from the dropdown (e.g., City_Name).
2. Under "8. Advanced Engine", click the purple Unique Column (Multiple Replace) button, then click the purple Mapping button at the bottom.
3. Choose the secondary column you want to overwrite from the "Select Target Column" prompt.
4. Use the top Search values bar for smart filtering if needed.
5. Edit the mapped values on the right side using Manual Typing, Left-to-Right Drag & Drop, or Right-to-Right Drag & Drop.
6. Click the green OK button and confirm with Yes on the overwrite warning dialog.

Method 2: Using the Right-Click Context Menu (Shortcut)

• Right-click the base Column Header -> Select 🗺️ Mapping -> Choose target column to overwrite -> Edit values via typing or drag-and-drop -> Click OK and confirm with Yes.

The Set Colour tool allows you to visually highlight specific data by applying color formatting to individual cells or entire rows based on their unique values.

⚠️ Important Export Note: Color formatting is strictly supported only when exporting as an Excel file (.xlsx or .xls). If exported as a CSV, all colors will be lost.

Color Modes & Rules:

• Strict Sequence: You must follow the order: Step 1: Select Cell, Step 2: Select Colour, Step 3: Select Mode. (Clicking a color before selecting a value triggers a "Select Cell First" warning).
• Single vs Row: Single applies color only to the specific individual cell (default). Row applies color across the entire row. (They are mutually exclusive).
• Clear Button: The white Clear button removes existing colors based on your selected mode.

Method 1: Using the Manual Data Cleaning Laboratory

1. Select target column -> Under "8. Advanced Engine", click Unique Column (Multiple Replace), then click Set Colour.
2. Step 1: Click a specific item from the left list. Step 2: Click a color circle (Red, Yellow, Green) or the white Clear circle. Step 3: Leave Single ticked or click Row.
3. Click the green OK button and confirm with Yes to apply the formatting.

Method 2: Using the Right-Click Context Menu (Shortcut)

• Right-click Column Header -> Select 🎨 Set Colour -> Follow the strict sequence (Select Cell -> Select Colour -> Select Mode) -> Click OK and confirm with Yes.

The Fill by Condition tool creates a new column based on logical conditions applied to a numerical column (e.g., assigning "Good" for >= 80). Note: This tool works only on numerical columns; text or date columns will trigger a warning.

Method 1: Using the Manual Data Cleaning Laboratory

1. Select the numerical target column from the dropdown.
2. Under "8. Advanced Engine", click Unique Column (Multiple Replace), then click Add Column at the bottom.
3. Enter a name for the new column (e.g., Grade) and click OK.
4. Click the orange Fill by Condition button at the bottom left.
5. Define your logic rules in the configuration window (enter threshold Value, select Operator like =, >, <, >=, <=, and set the Output text). Use the Light Blue + and Red - buttons to manage rows, then click OK.
6. Review the updated Advanced Engine window and click the green OK button at the bottom right.
7. Confirm with Yes to permanently add the new column to the right of your base column.

Method 2: Using the Right-Click Context Menu (Shortcut)

• Right-click the target numerical Column Header -> Select ⚙️ Fill by Condition -> Enter new column name and click OK -> Configure conditions in the window -> Click OK -> Click Apply Change and confirm with Yes.

The Fill by Logic tool allows you to populate a column's cells by writing a custom mathematical formula or logic. Validation Warning (Shortcut): Must be applied to a completely blank column.

Method 1: Using the Right-Click Context Menu (Shortcut)

1. Right-click the Column Header of a completely blank column.
2. Select 🧠 Fill by Logic to open the Formula Builder window.
3. Type your formula in the Input Logic box utilizing the smart autocomplete feature and Special Sign buttons.
4. Verify real-time results in the Live Preview (Row 1) section at the bottom.
5. Click the green OK button to execute the calculation and update the dataset.

Method 2: Using the Manual Data Cleaning Laboratory

• Select any reference column -> Under "8. Advanced Engine", click Unique Column (Multiple Replace) -> Click Add Column and name it -> Click the pink Fill by Logic button -> Build formula in the Formula Builder -> Review Live Preview, click OK, and confirm with Yes.

Important Placement Note: The newly created column filled with your logic will be placed exactly to the right side of the selected base column.

The Fill Date feature allows you to automatically fill missing or blank date entries using the preceding valid dates (forward fill) in your dataset.

Steps to Use:

1. Access the Tool: Right-click on the specific date column you want to modify to open the context menu and select 📅 Fill Date.
2. Validation Check: The system instantly verifies if the column is a valid Date or Datetime format. Selecting a non-date column will trigger a warning message.
3. Execution: Once clicked on a valid date column, the engine instantly propagates the previous valid date downward to fill any subsequent blank date cells in that column.

The Date Diff feature calculates the difference between two dates to find durations, ages, or tenures, and outputs the result in a brand-new column.

Steps to Use:

1. Access the Tool: Right-click on a valid Date column in your table and select ⏳ Date Diff from the context menu.
2. Configuring the Output: In the Calculate Date Difference modal, enter a New Column Name and select your preferred output format from the Difference Unit dropdown: Years (Age), Months, or Days.
3. Selecting the Target Date: Choose your comparison method using the Compare With dropdown ("Use Another Date Column" or "Use Custom Date"), or enter a specific date in the Compare Date (YYYY-MM-DD) field if no other date columns exist.
4. Execution: Click Calculate to compute the differences and insert the new column immediately to the right of your originally selected column (or click Cancel to abort).

The Fill Series feature allows you to generate auto-incrementing serial numbers, custom identifiers (e.g., Employee IDs), or sequential series. Validation Warning: Must be applied to a completely empty (blank) column.

Steps to Use:

1. Access the Tool: Right-click on any completely empty (blank) column in your table and select 🔢 Fill Series from the context menu.
2. Setting Up the Series: In the Fill Series modal, enter your starting number in the Start from field (e.g., 1 or 001), select your increment interval from the Add Gap dropdown, and optionally add text using the Add Prefix and Add Suffix fields.
3. Execution: Click Fill to generate the sequence down the entire length of the column (or click Cancel to abort).

The Change Format feature allows you to safely change the data type of a specific column while preventing accidental data loss. Note: This tool cannot be used on existing Date columns.

Steps to Use:

1. Access the Tool: Right-click on the column you wish to modify and select 🔀 Change Format from the context menu.
2. Selecting the Target Format: In the Change Format modal, click the Select Format dropdown and choose your new target format: Integer, Decimal, Boolean, or Text/String.
3. Safety Check & Execution: Click Apply. If converting Text to Numbers, an automatic safety scan checks the sample data; if impossible, it blocks the action with a warning. Otherwise, the format updates instantly (click Cancel to abort).

The Filter tool works exactly like the filtering feature in Microsoft Excel, allowing you to easily sort data and isolate specific values.

Steps to Apply Filter Tags:

1. Step 1: Select your target column. You can click on a single column, or highlight multiple columns at the same time to apply filter tags to all of them at once.
2. Step 2: Right-click directly on the highlighted Column Header(s).
3. Step 3: Select 🔍 Filter from the context menu.
4. Step 4: A small black dropdown triangle icon will appear next to the column name(s), indicating that the filter tag has been successfully applied.

Once the filter tag is applied, you can use the interactive Filter Panel to organize your data.

Steps to Use the Filter Panel:

1. Left-click on the black dropdown triangle icon on the column header.
2. Sort Data: Click Sort A to Z or Sort Z to A to organize your column alphabetically or numerically.
3. Search & Select: Use the Search text box to quickly locate specific items, and tick/untick checkboxes (or use (Select All)) to dictate what remains visible.
4. Click the OK button at the bottom. The main data table will instantly update to display only the filtered rows.

To clear filters and restore your full dataset:

Steps to Unfilter:

1. Select the column or multiple columns that currently have active filter tags applied.
2. Right-click on the highlighted Column Header(s).
3. Select 🚫 Unfilter from the context menu. The dropdown tags will disappear, and all hidden rows will become visible again.

Application Initialization (Splash Screen):

• Launching GDLytics opens an animated startup splash screen displaying "Initializing Application..." with a progress loading bar.
• Once internal background processes, database connections, and libraries are loaded, the splash screen closes automatically and launches the User Login window.

On a fresh installation, the system comes pre-configured with default administrative credentials:

• Username: admin
• Password: 1234

Note: These credentials remain active until updated through the Settings menu.

User Login Controls:

• Username Field: Enter your registered account username.
• Password Field: Enter your account password. Click the Eye Icon on the right side to toggle between hidden masked dots and plaintext view.
• Sign in (Green Button): Authenticates entered credentials and opens the Home workspace.
• Reset (Blue-Teal Button): Instantly clears both the Username and Password input fields for fresh entry.

Recovery Prerequisite: Clicking "Forgot Password?" sends an OTP recovery email with your username only if a valid recovery Gmail address was previously configured and saved in the Settings page.

No Recovery Email Configured: If a user updates their password, forgets the new credentials, and never configured a recovery Gmail address in Settings, self-service password recovery is not possible. In this scenario, the application must be uninstalled and reinstalled to reset the account back to the default credentials (admin / 1234).

The Logout button is located at the bottom of the left sidebar navigation menu, providing a secure way to end the active user session.

Action & Confirmation:

• Trigger: Clicking the Logout button opens a confirmation dialog asking: "Are you sure?"
• Yes: Confirms session termination, logs out the current user, and immediately redirects to the Login screen.
• No / Cancel: Dismisses the dialog without taking any action, keeping you securely inside the current workspace.