Traditional query systems struggle with large-scale, complex data operations involving pagination, aggregation, and multi-source joins. Tight coupling of query parsing and execution leads to inefficiencies like over-fetching, high latency, and rising LLM costs, limiting scalability and flexibility in evolving data environments.
Key challenges
Tight coupling of query components
Inefficient multi-source data joins
Over-fetching and under-fetching of data
The solution
Intelligent orchestration
LLM intent detection
Selective tool activation
Reduced unnecessary calls
Dynamic execution decisions
Scalable execution
Multi-source joins
Server-side computation
Streaming-first delivery
Pagination + aggregation engine
1
Smart design
Modular
Understand intent
No rigid integrations
Handle aggregation/pagination
2
Efficient processing
Guided workflows
Conditional logic
Optimized and scalable
Real-time structured results
Performance gains
Lower latency
Real-time feedback
Faster query execution
Cost efficiency
Reduced LLM usage
Optimized compute load
Minimal redundant calls
Scalability
Handles large datasets
Multi-source flexibility
Adapts to query complexity
