Visual Workflow Builder

Intelligent Data Workflow Builder

Design complex data pipelines with our intuitive drag-and-drop interface. Connect to any data source and leverage AI-powered optimization recommendations.

Agentic Workflow Enhancement

Our AI constantly analyzes your workflow patterns and suggests optimizations for performance, cost, and reliability. The more you build, the smarter it gets.

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Saved Workflows
Customer Churn Analysis
5 nodes
Network Performance Dashboard
8 nodes
Regulatory Compliance Report
6 nodes
Service Quality Monitoring
7 nodes
Revenue Assurance Pipeline
9 nodes
Workflow Canvas
Customer Churn Analysis Pipeline
Customer Database
PostgreSQL
Active Customers
Filter: status = 'active'
Join Usage Data
Inner join on customer_id
Usage Data
PostgreSQL
Churn Prediction
ML model: XGBoost
Results Storage
Save to analytics database
Churn Prediction

Configure the selected node properties

Ctrl+click to select multiple features
AI Recommendations
Feature Recommendation

Based on telecom industry patterns, adding international_calls and support_interactions could improve model accuracy by ~4.2%.

Model Selection

For your dataset size (~500K records), XGBoost typically outperforms other models for churn prediction in telecom data.

Data Quality Alert

Your input data has ~3.5% missing values in call_minutes. Consider adding a preprocessing step.

Performance Estimation
91.4%
Accuracy
0.88
AUC-ROC
86.2%
Precision
83.9%
Recall
Workflow AI Assistant
I need to build a workflow to predict customer churn for our telecom data.

I'll help you build a customer churn prediction workflow for telecom data. Here's what I recommend:

  1. Start with data sources: Customer profile database and usage data
  2. Add a filter to focus on active customers
  3. Join customer profiles with usage data
  4. Add preprocessing steps for data cleaning and feature engineering
  5. Use the Churn Prediction component with XGBoost (best for telecom data)
  6. Output results to a database and visualization

Would you like me to create this workflow for you?

Yes, please create it. Also, what features are most important for telecom churn prediction?

I've created a basic churn prediction workflow. For telecom specifically, these features are typically most predictive:

  • Usage patterns: Call minutes, data usage, SMS counts
  • Customer service: Number of complaints, support calls
  • Account information: Contract length, billing amount
  • Demographics: Age, location (urban/rural)
  • Network experience: Dropped calls, service quality

I've included most of these in your workflow. Would you like me to add any other specific telecom metrics?

Can you add network quality metrics to the model? We have data on dropped calls and signal strength.

I've updated your workflow to include network quality metrics. I've added:

  1. A new data source node for network quality data
  2. A join operation to connect it with your customer data
  3. Additional features: dropped_calls_pct, avg_signal_strength, and network_issues_reported

These network quality metrics typically improve churn prediction accuracy by 5-8% in telecom models. Would you like to also add a feature importance visualization to see how these metrics compare to other features?

Ask anything about building your workflow or optimizing it
Workflow Validation Successful

All nodes and connections have been validated with no errors detected.

Validation Results
Node Status Message
Customer Database Valid Connection successful, schema validated
Active Customers Valid Filter condition valid
Usage Data Valid Connection successful, schema validated
Join Usage Data Valid Join keys compatible
Churn Prediction Warning Missing values detected in 'call_minutes' column (3.5%)
Results Storage Valid Connection successful, write permissions verified
Data Sample Preview
customer_id subscription_length call_minutes data_usage customer_complaints churned
C10045 24 320.5 4.2 1 0
C10046 6 150.2 8.7 3 1
C10047 36 421.8 2.5 0 0
C10048 12 null 6.1 2 1
C10049 18 215.6 3.9 1 0
Workflow Execution

Run your workflow and monitor execution progress

Execution Configuration
Execution History
Execution ID Start Time Duration Status Records Processed Actions
WF-20250517-001 2025-05-17 08:30:15 12m 24s Completed 524,387
WF-20250516-003 2025-05-16 15:45:22 14m 05s Completed 524,102
WF-20250516-002 2025-05-16 10:12:08 4m 36s Failed 126,544
WF-20250516-001 2025-05-16 08:30:45 13m 17s Completed 523,987