BI was never designed for AI
Legacy BI tools bolt on AI as an afterthought — a chatbot sidebar on a drag-and-drop builder designed in 2012. The result: fragile NLP wrappers over rigid data models that still require a data team to maintain, update, and debug.
AI-native from day one
Datural doesn't add AI to BI. It replaces the manual pipeline entirely. Ask a question. Get a dashboard. The system writes the SQL, picks the chart type, handles cross-source joins, and learns from every interaction.
From question to dashboard
Ask
Type a question in plain English. No SQL, no chart builder, no data model required.
Resolve
Datural maps your question to the semantic graph, picks the right sources, and writes optimized SQL.
Visualize
The system selects the best chart type, renders it live, and drops it into a grid you can rearrange.
Learn
Every question makes the system smarter. Cached routes mean repeat queries skip the LLM entirely.
One prompt, full dashboard
Describe what you need in a sentence. Datural generates a complete dashboard grid — bars, lines, metrics, donuts — in seconds, not sprints.
Cross-source intelligence
A single question can pull from Snowflake, Postgres, and MongoDB simultaneously. No ETL pipelines or data warehouse required.
Transparent SQL
Every chart shows the SQL that generated it. Click to inspect, copy, or modify. Full auditability, zero black boxes.
Self-service for everyone
Product managers, marketing leads, and executives can explore data without filing a ticket. The data team focuses on architecture, not ad-hoc requests.
Adaptive chart selection
Datural picks the right visualization for the data shape: time series get lines, comparisons get bars, proportions get donuts. Override anytime.
Live and always current
Dashboards query live data. No stale extracts, no scheduled refreshes. Ask now, see now.
Dashboard creation
Before: 2-week sprint with data team
After: one sentence, 10 seconds
Ad-hoc analysis
Before: Slack the analyst, wait 2 days
After: ask Datural directly
Cross-source reports
Before: build ETL pipeline, wait for sync
After: one query across all sources
Metric consistency
Before: 6 tools, 6 different definitions
After: one semantic graph, one truth