Conversational Business Intelligence for GA4, Shopify, and Stripe through Natural Language.
AskAnalytics represents the 2026 standard in conversational data interfaces, abstracting the complexity of modern analytics platforms like GA4 and Snowflake into a natural language layer. Built on a proprietary RAG (Retrieval-Augmented Generation) architecture optimized for structured query generation, the tool allows non-technical stakeholders to perform complex cross-channel attribution and trend analysis without writing SQL or navigating nested UI menus. Its technical core utilizes a semantic mapping engine that interprets business logic (e.g., 'Churn Rate' or 'LTV') and translates it into precise API calls across disparate data silos. Positioned as a mission-critical middle layer in the modern data stack, it provides automated anomaly detection and predictive forecasting, enabling organizations to move from reactive reporting to proactive strategy. The platform's 2026 roadmap emphasizes 'Autonomous Insights,' where the AI proactively flags statistical deviations and suggests optimization paths before a human even asks a question, effectively functioning as a virtual data analyst available 24/7.
A middleware mapping that translates business definitions into complex multi-join SQL queries automatically.
Convert natural language prompts into high-performance SQL for the Atlassian Data Lake.
Conversational Business Intelligence for deep-dive data exploration and predictive forecasting.
Turn Complex Natural Language into Production-Ready SQL and Executive Insights Instantly
Turn complex data warehouses into conversational intelligence engines using agentic RAG and NLQ.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Uses isolation forests and time-series decomposition to identify statistical outliers in real-time.
Applies Prophet and ARIMA models to historical data to project future performance metrics.
Virtually joins data from different APIs (e.g., Facebook Ad Spend vs Shopify Sales) in the cloud.
Translates raw data visualizations into natural language executive summaries.
Generates real-time UI layouts based on the context of the user's natural language question.
Allows technical users to view and copy the raw SQL code generated by the AI.
Manual tracking of hourly sales during Black Friday is inefficient and error-prone.
Registry Updated:2/7/2026
Difficulty understanding which paid channels contribute most to high-LTV customers.
Customer success teams reacting too late to user churn.