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
How It Works
How It Works
- Question Input: You ask a question in plain English
- Intent Recognition: AI understands what you’re looking for
- Schema Analysis: AI examines your database structure
- Query Generation: AI creates the appropriate SQL/NoSQL query
- Execution: Query runs against your database
- Results: You get answers with visualizations
Example Conversations
Example Conversations
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
Schema Understanding
Schema Understanding
- 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
Context Awareness
Context Awareness
- 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
Query Optimization
Query Optimization
- 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
Basic Analytics
Basic Analytics
Complex Joins
Complex Joins
Time-Series Analysis
Time-Series Analysis
Cross-Database Queries
Cross-Database Queries
AI Capabilities
Natural Language Understanding
Natural Language Understanding
- 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
Business Intelligence
Business Intelligence
- 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
Data Exploration
Data Exploration
- 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
Advanced Model Capabilities
Advanced Model Capabilities
- 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
Security & Compliance
Security & Compliance
- 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
Performance & Scalability
Performance & Scalability
- 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
Adaptive Learning
Adaptive Learning
- 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
Continuous Improvement
Continuous Improvement
- 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
Writing Effective Questions
Writing Effective Questions
- 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”
Working with AI
Working with AI
- 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.
Current Limitations
Current Limitations
- 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
When to Use Manual Queries
When to Use Manual Queries
- 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.
Your First AI Query
Your First AI Query
- Connect your database (if not already done)
- Ask a simple question: “Show me total sales”
- Review the results: Check if the answer makes sense
- Ask follow-ups: “Break this down by month”
- Explore further: “Which products are selling best?”
Tips for Success
Tips for Success
- 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.
Custom AI Training
Custom AI Training
Train AI models on your specific business terminology and data patterns for enhanced accuracy and relevance.
AI Consulting
AI Consulting
- 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