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SyneHQ’s AI understands your database schema and provides intelligent assistance for query writing, optimization, and data exploration. Our enterprise-grade AI models deliver accurate, reliable, and contextually aware natural language processing.

Natural Language Processing

  1. Question Input: You ask a question in plain English
  2. Intent Recognition: AI understands what you’re looking for
  3. Schema Analysis: AI examines your database structure
  4. Query Generation: AI creates the appropriate SQL/NoSQL query
  5. Execution: Query runs against your database
  6. Results: You get answers with visualizations
User: “Show me our top customers by revenue” AI: Analyzes customer and order tables, generates SQL query Result: Table of customers with total revenue, sorted descendingUser: “Which marketing campaigns are performing best?” AI: Joins campaign, user, and conversion tables Result: Chart showing campaign performance metricsUser: “What’s our monthly growth rate?” AI: Calculates month-over-month growth from time-series data Result: Line chart with growth trends and percentage changes

Smart Features

  • Automatic Discovery: AI learns your database structure
  • Relationship Mapping: Understands table relationships and foreign keys
  • Data Type Recognition: Knows how to handle different data types
  • Business Logic: Learns your business terminology and metrics
  • Conversation Memory: Remembers previous questions in the session
  • Follow-up Questions: “Show me more details” works naturally
  • Query Refinement: “Filter by last 30 days” modifies previous results
  • Cross-Reference: Can reference previous results in new queries
  • Performance Analysis: Suggests faster query approaches
  • Index Recommendations: Advises on database optimization
  • Query Rewriting: Automatically optimizes generated queries
  • Resource Management: Efficient memory and CPU usage

Query Types Supported

-- Natural language: "Show me total sales by month"
SELECT 
  DATE_TRUNC('month', order_date) as month,
  SUM(amount) as total_sales
FROM orders 
GROUP BY month 
ORDER BY month;
-- Natural language: "Which products are most popular with premium customers?"
SELECT 
  p.name,
  COUNT(o.id) as order_count
FROM products p
JOIN order_items oi ON p.id = oi.product_id
JOIN orders o ON oi.order_id = o.id
JOIN customers c ON o.customer_id = c.id
WHERE c.tier = 'premium'
GROUP BY p.id, p.name
ORDER BY order_count DESC;
-- Natural language: "Show me daily active users for the last 30 days"
SELECT 
  DATE(created_at) as date,
  COUNT(DISTINCT user_id) as daily_active_users
FROM user_sessions
WHERE created_at >= NOW() - INTERVAL '30 days'
GROUP BY DATE(created_at)
ORDER BY date;
-- Natural language: "Compare user data from our main app and mobile app"
SELECT 
  pg_users.name,
  pg_users.email,
  mobile_users.last_login
FROM postgres_db.public.users pg_users
JOIN mysql_db.mobile.users mobile_users
  ON pg_users.email = mobile_users.email;

AI Capabilities

  • Question Types: What, how, when, where, why questions
  • Comparative Analysis: “Better than”, “worse than”, “compared to”
  • Temporal Queries: “Last week”, “this month”, “year over year”
  • Aggregations: Sum, average, count, percentage, growth rate
  • KPI Calculation: Revenue, conversion rates, customer lifetime value
  • Trend Analysis: Growth patterns, seasonality, anomalies
  • Segmentation: Customer groups, product categories, geographic regions
  • Forecasting: Simple predictions based on historical data
  • Schema Discovery: “What tables do we have?”
  • Data Profiling: “Show me sample data from the users table”
  • Quality Checks: “Are there any missing values in the orders table?”
  • Relationship Mapping: “How are customers and orders related?”

Enterprise AI Features

  • Custom Training: Train models on your specific business terminology
  • Domain Adaptation: Adapt AI to your industry-specific language
  • Multi-Language Support: Support for multiple languages and dialects
  • Context Learning: AI learns from your team’s query patterns
  • Data Privacy: AI models don’t store or learn from sensitive data
  • Audit Trails: Complete logging of AI-generated queries
  • Approval Workflows: Optional approval for AI-generated queries
  • Custom Security: Integrate with your security and compliance systems
  • Model Optimization: Optimized for enterprise-scale workloads
  • Caching: Intelligent caching of AI model responses
  • Load Balancing: Distributed AI processing across multiple instances
  • Resource Management: Efficient GPU and memory utilization

Learning & Improvement

  • User Feedback: Learns from your corrections and preferences
  • Query History: Improves based on successful query patterns
  • Schema Changes: Automatically adapts to database modifications
  • Business Context: Understands your industry-specific terminology
  • Model Updates: Regular AI model improvements
  • Feature Enhancements: New query capabilities added over time
  • Performance Optimization: Faster and more accurate query generation
  • Language Support: Additional languages and dialects

Best Practices

  • Be Specific: “Show me sales from Q1” vs “Show me sales”
  • Use Business Terms: “Premium customers” vs “customers with tier = ‘premium’”
  • Provide Context: “Compared to last year” vs just “growth”
  • Ask Follow-ups: “Show me more details” or “Break this down by region”
  • Start Simple: Begin with basic questions, then add complexity
  • Use Natural Language: Don’t try to write SQL-like queries
  • Provide Feedback: Correct the AI when it makes mistakes
  • Explore Gradually: Build up to complex multi-table analysis

Limitations & Considerations

While SyneHQ’s AI is powerful, understanding its limitations helps you use it most effectively.
  • Complex Business Logic: Very specific business rules may need manual queries
  • Custom Functions: Database-specific functions may not be supported
  • Performance Tuning: Very large datasets may need optimization
  • Real-time Data: Some real-time features may have slight delays
  • Highly Optimized Queries: When performance is critical
  • Custom Aggregations: Complex business calculations
  • Database-Specific Features: Using database-specific functions
  • Debugging: When you need to see the exact SQL being generated

Getting Started

Ready to start using AI features? Follow these steps to get the most out of natural language querying.
  1. Connect your database (if not already done)
  2. Ask a simple question: “Show me total sales”
  3. Review the results: Check if the answer makes sense
  4. Ask follow-ups: “Break this down by month”
  5. Explore further: “Which products are selling best?”
  • Start with familiar data: Query tables you understand well
  • Use business language: Speak naturally, not technically
  • Be patient: AI improves with more usage and feedback
  • Ask for help: Use the “Explain this query” feature to learn

Enterprise AI Support

For enterprise customers, we offer custom AI model training and specialized support for your specific business needs.
Train AI models on your specific business terminology and data patterns for enhanced accuracy and relevance.
  • Query Optimization: Expert guidance on writing effective natural language queries
  • Schema Optimization: Optimize your database schema for better AI understanding
  • Business Logic: Help AI understand your specific business rules and KPIs
  • Team Training: Train your team on effective AI usage

AI Query Examples

See more examples of natural language queries
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