Arria NLG
The global leader in deterministic Natural Language Generation for data-driven storytelling.
Transform complex database schemas into actionable natural language insights with autonomous SQL synthesis.
IntelliQuery is a high-performance, enterprise-grade AI solution designed to bridge the technical gap between business stakeholders and relational databases. By utilizing a sophisticated multi-stage Retrieval-Augmented Generation (RAG) architecture, IntelliQuery indexes database metadata—not the sensitive data itself—to generate precise SQL queries from natural language prompts. As of 2026, the platform has matured to support autonomous schema discovery and semantic mapping, allowing it to interpret ambiguous join logic across disparate tables with 94%+ accuracy. Its technical core integrates with LLMs like GPT-4o and Claude 3.5 Sonnet, optimized specifically for DDL and dialect-specific syntax (PostgreSQL, Snowflake, BigQuery, and SQL Server). Market-positioned as a middleware layer for democratized analytics, it solves the 'analyst bottleneck' by providing instant data access while maintaining strict SOC2-compliant governance. The platform's 2026 roadmap includes 'Predictive Query Optimization' and 'Auto-Visualization' which suggests the best chart types based on the returned data structure, making it a critical tool for data-driven organizations aiming for real-time intelligence without manual reporting cycles.
Automatically detects SQL errors upon execution and re-prompts the LLM with the error trace to provide a corrected script instantly.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Builds a vector-based graph of business definitions that maps 'Revenue' to complex calculations across multiple tables.
Uses Regex and NLP to identify and mask PII/PHI columns during the schema crawling phase before they reach the LLM.
Translates legacy Oracle or SQL Server queries into modern Snowflake or BigQuery syntax.
Virtualizes data across separate databases (e.g., Postgres and MongoDB) to allow unified natural language querying.
Provides a step-by-step breakdown in plain English of how the AI interpreted the question and why it joined specific tables.
Analyzes query patterns to suggest where database indexes should be added to improve performance.
Sales VPs needing instant regional data without waiting for the BI team to build a Looker dashboard.
Registry Updated:2/7/2026
Auditors spending hours matching transaction records across different ledgers.
Difficulty in joining web traffic data with CRM sales data for non-technical marketers.