Emotion Analysis
Description: Analyze customer emotions from text data such as Emails and Reviews
URL: POST https://cogxta.ai/ai/analyze_emotion{email}
Input Format:
{
"email": "Thanks for the resolution..should do faster but I am very happy about the outcome."
}
Output Format:
{
"Primary_Emotion": "Happiness",
"Secondary_Emotion": "Relief",
"Reason": "Positive outcome, slight frustration",
"Sentiment": "Positive",
"Recommendation": "Acknowledge speed concerns, maintain positive tone",
"Emotion_score": "8/10"
}
Anomaly Detection
Description: Detect anomalies in data for proactive measures, such as in transaction data, server logs, or user behavior.
URL: POST https://cogxta.ai/ai/detect_anomaly{data}
Input Format:
{
"data": [
{"timestamp": "2025-03-11T09:15:30", "value": 102.5},
{"timestamp": "2025-03-11T09:16:00", "value": 110.7},
{"timestamp": "2025-03-11T09:16:30", "value": 98.3}
]
}
Output Format:
{
"Anomalies_Detected": true,
"Anomaly_Timestamp": "2025-03-11T09:16:00",
"Anomaly_Type": "Sudden Spike",
"Severity_Score": "9/10",
"Recommendation": "Investigate possible system errors or suspicious activities"
}
Recommendation Engine
Description: Provide personalized product or content recommendations based on user behavior and preferences.
URL: POST https://cogxta.ai/ai/recommend{user_data}
Input Format:
{
"user_id": "12345",
"recent_activity": [
{"product_id": "A1001", "action": "viewed"},
{"product_id": "B2002", "action": "purchased"},
{"product_id": "C3003", "action": "added_to_cart"}
]
}
Output Format:
{
"Recommendations": [
{"product_id": "D4004", "product_name": "Wireless Headphones", "reason": "Similar to items in cart"},
{"product_id": "E5005", "product_name": "Bluetooth Speaker", "reason": "Frequently bought with purchased item"}
],
"Recommendation_score": "9/10",
"Personalization_notes": "Based on past interactions and preferences"
}
Model Audit Service
Description: Audit machine learning models to ensure fairness, accuracy, and compliance with ethical guidelines and regulatory standards.
URL: POST https://cogxta.ai/ai/audit_model{model_data}
Input Format:
{
"model_name": "Customer_Churn_Predictor",
"model_version": "v1.2",
"training_data": {
"features": ["age", "income", "region", "subscription_type"],
"target": "churn"
},
"evaluation_metrics": {
"accuracy": 0.85,
"precision": 0.78,
"recall": 0.80
}
}
Output Format:
{
"Audit_Result": "Passed with Warnings",
"Fairness_Issues": [
{"feature": "region", "issue": "Potential bias detected in specific region clusters"},
{"feature": "income", "issue": "Significant disparity in prediction accuracy for different income levels"}
],
"Performance_Score": "7/10",
"Recommendation": "Refine model with more balanced data, particularly across regions and income groups"
}
Causal Analysis
Description: Perform causal analysis to estimate the impact of interventions or treatments on key outcomes based on observational data.
URL: POST https://cogxta.ai/ai/causal_analysis{data}
Input Format:
{
"treatment": "marketing_campaign",
"outcome": "sales",
"covariates": {
"region": "North America",
"seasonality": "Q4",
"past_sales": 50000
},
"data_source": "sales_data.csv"
}
Output Format:
{
"Estimated_Treatment_Effect": 12.5,
"Confidence_Interval": [10.2, 14.8],
"P_value": 0.03,
"Significance": "Statistically significant",
"Recommendation": "Continue marketing campaign in the same region for Q1 based on positive impact on sales"
}
NL Data Query
Description: Query structured datasets using natural language to retrieve insights without writing SQL or complex queries.
URL: POST https://cogxta.ai/ai/nl_data_query{query}
Input Format:
{
"query": "What were the total sales for Q4 2024 in the North America region?",
"dataset": "sales_data.csv"
}
Output Format:
{
"Result": {
"Total_Sales": 750000,
"Region": "North America",
"Quarter": "Q4 2024"
},
"Query_Interpretation": "Total sales in the North America region for the fourth quarter of 2024",
"Confidence_Score": "9.5/10"
}
Log Data Analysis
Description: Analyze system or application log data to identify critical errors, recurring patterns, and performance issues over time.
URL: POST https://cogxta.ai/ai/log_data_analysis{logs}
Input Format:
{
"log_entries": [
{"timestamp": "2025-03-10T09:15:34", "level": "ERROR", "message": "Failed to load user profile"},
{"timestamp": "2025-03-10T09:16:10", "level": "WARNING", "message": "API response time exceeded threshold"},
{"timestamp": "2025-03-10T09:18:22", "level": "INFO", "message": "User logged in successfully"},
{"timestamp": "2025-03-10T09:19:47", "level": "ERROR", "message": "Payment gateway connection timed out"},
{"timestamp": "2025-03-10T09:20:52", "level": "INFO", "message": "Transaction ID #456 approved"},
{"timestamp": "2025-03-10T09:23:06", "level": "ERROR", "message": "Database read timeout"},
{"timestamp": "2025-03-10T09:24:31", "level": "WARNING", "message": "CPU usage exceeded 80%"},
{"timestamp": "2025-03-10T09:26:15", "level": "ERROR", "message": "Failed to send email notification"},
{"timestamp": "2025-03-10T09:30:45", "level": "INFO", "message": "User logged out"},
{"timestamp": "2025-03-10T09:35:11", "level": "ERROR", "message": "File upload failed due to invalid format"}
]
}
Output Format:
{
"Analysis_Result": {
"Total_Entries": 10,
"Error_Count": 5,
"Warning_Count": 2,
"Info_Count": 3,
"Top_Issues": [
{"issue": "Database read timeout", "frequency": 2},
{"issue": "Failed to load user profile", "frequency": 1},
{"issue": "Payment gateway connection timed out", "frequency": 1},
{"issue": "Failed to send email notification", "frequency": 1}
],
"Performance_Warnings": [
{"issue": "API response time exceeded threshold", "occurrence": "2025-03-10T09:16:10"},
{"issue": "CPU usage exceeded 80%", "occurrence": "2025-03-10T09:24:31"}
]
},
"Critical_Issues": [
{"issue": "Database read timeout", "recommendation": "Optimize database queries or increase timeout limit"},
{"issue": "Payment gateway connection timed out", "recommendation": "Check network or retry strategy for payment gateway"},
{"issue": "Failed to send email notification", "recommendation": "Investigate email server or API issues"}
],
"Recommendations": [
"Increase API response threshold or optimize API call response time",
"Monitor CPU usage closely and add auto-scaling if required"
]
}
Document Analysis
Description: Analyze documents to extract key information, detect themes, classify content, and identify issues such as missing sections or incomplete data.
URL: POST https://cogxta.ai/ai/document_analysis
Input Format: Accepts either text or document file input
{
"text_data": "text data from document"
}
Output Format:
{
"Extracted_Sections": {
"Introduction": "Attached is the annual financial report.",
"Financial_Performance": "Revenue has grown steadily over the past three quarters, but operating costs have risen.",
"Recommendations": "Key recommendations are to improve cost efficiency and diversify revenue streams."
},
"Missing_Sections": ["Conclusion"],
"Classifications": [
{"Category": "Finance", "Confidence": "98%"},
{"Category": "Strategy", "Confidence": "87%"}
],
"Detected_Issues": [
{"Issue": "Incomplete report", "Recommendation": "Include missing conclusion and further analysis results"}
]
}
Customer Profiling
Description: Create customer profiles based on input data such as purchase history, demographics, and behavioral patterns. The API generates insights about customer preferences, segments, and recommendations for personalized offers.
URL: POST https://cogxta.ai/ai/customer_profiling
Input Format: Provide customer data (e.g., demographics, purchase history)
{
"customer_id": "CUST12345",
"name": "John Doe",
"age": 32,
"gender": "Male",
"location": "New York",
"purchase_history": [
{"item": "Smartphone", "amount": 699.99, "date": "2025-01-15"},
{"item": "Headphones", "amount": 199.99, "date": "2025-02-05"},
{"item": "Laptop", "amount": 1299.99, "date": "2025-03-01"}
],
"browsing_history": [
{"item": "Smartwatch", "category": "Wearables"},
{"item": "Tablet", "category": "Electronics"}
]
}
Output Format:
{
"Customer_ID": "CUST12345",
"Profile": {
"Age_Group": "30-35",
"Gender": "Male",
"Preferred_Category": "Electronics",
"Purchase_Pattern": "High-Value Purchases",
"Location": "New York"
},
"Customer_Segment": "Tech Enthusiast",
"Recommended_Offers": [
{"Offer": "20% off on Smartwatches", "Category": "Wearables"},
{"Offer": "Free Accessories with Tablet Purchase", "Category": "Electronics"}
],
"Churn_Risk": "Low",
"Next_Best_Action": "Send personalized offers for high-end electronics."
}
Execute Workflows with Chatbot Service
Description: This API combines the functionality of executing internal workflows and interacting with a chatbot service. Employees can initiate workflows, such as generating reports or sending updates, through the chatbot interface. The chatbot can provide responses, track workflow progress, and suggest further actions.
URL: POST https://cogxta.ai/ai/execute_workflow_chatbot
Input Format: Provide the user query and workflow details (JSON Format)
{
"customer_id": "EMP12345",
"query": "Can you generate the monthly sales report and send it to the finance department?",
"context": {
"workflow_id": "WF001",
"workflow_name": "Monthly_Report_Generation",
"initiator": {
"user_id": "EMP12345",
"department": "Sales",
"role": "Sales Manager"
},
"tasks": [
{
"task_name": "Generate_Sales_Report",
"parameters": {
"start_date": "2025-02-01",
"end_date": "2025-02-28",
"report_format": "PDF"
}
},
{
"task_name": "Send_Report_Email",
"parameters": {
"recipient": "finance_dept@company.com",
"subject": "Monthly Sales Report",
"attachment": "Sales_Report_Feb_2025.pdf"
}
}
]
}
}
Output Format:
{
"Response": {
"Customer_ID": "EMP12345",
"Answer": "The monthly sales report for February 2025 has been generated and sent to the finance department.",
"Next_Step": "Would you like to track the report status or generate another report?"
},
"Workflow_Execution": {
"workflow_id": "WF001",
"workflow_status": "Completed",
"tasks_executed": [
{
"task_name": "Generate_Sales_Report",
"status": "Success",
"output": "Sales_Report_Feb_2025.pdf"
},
{
"task_name": "Send_Report_Email",
"status": "Success",
"timestamp": "2025-03-03T10:15:00"
}
],
"execution_summary": "Workflow executed successfully. Sales report generated and emailed to finance department."
},
"Escalation_Required": false
}