"Show me the top 10 users by revenue this month." With Aurora Analytics, that's all you need to type. Our natural language interface converts questions into optimized queries and beautiful visualizations.
The Challenge
Most analytics tools require SQL knowledge or complex UI interactions. Non-technical users struggle to extract insights, creating bottlenecks.
NLP Pipeline Architecture
Our pipeline has four stages: parsing, schema mapping, query generation, and optimization.
class NLPQueryPipeline {
async process(question: string): Promise<Query> {
// 1. Parse intent and entities
const parsed = await this.parser.parse(question);
// 2. Map to database schema
const mapping = await this.mapper.mapToSchema(parsed);
// 3. Generate SQL
const sql = await this.generator.generate(mapping);
// 4. Optimize query
return this.optimizer.optimize(sql);
}
}Intent Recognition
We use fine-tuned language models to identify query type (aggregation, comparison, trend analysis) and extract key parameters (metrics, dimensions, time ranges).
Schema Mapping
The system learns your database schema and creates semantic mappings. It understands that "customers" might map to a "users" table, and "this month" means a WHERE clause on the date column.
Query Optimization
Generated queries are automatically optimized:
• Push filters down to database level
• Use appropriate indexes
• Cache common aggregations
• Parallelize independent subqueries
Natural language queries execute in an average of 510ms - faster than most hand-written SQL.
Visualization Selection
Aurora automatically chooses the best visualization based on query type. Time series get line charts, comparisons get bar charts, and distributions get histograms.
The result is an analytics experience that feels magical - ask a question and get an answer immediately, no SQL required.