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Ask in Plain English
Type questions like “Show average deposit balance by market for Q4” and Dialect understands intent instantly.
Bank Data Text-to-SQL Assistant
Powered by Agentic Query Intelligence
Introducing Dialect
Natural Language SQL Querying
for Banking Data
Ask questions in plain English and get production-ready SQL with explanations, validation checks, and guided refinement workflows.
Banking-focused semantic layer
Fast query generation
Validation-first workflow
Explainable SQL outputs
From Question to Insight in Seconds
No SQL expertise required. Ask a question, and our multi-agent pipeline handles the rest.
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Multi-Agent Intelligence
Nine specialized agents classify intent, fetch context, plan structure, and generate validated SQL.
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Execute & Visualize
Run generated SQL against live banking data and explore outputs through interactive tables and charts.
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Nine agents.
One question.
Your question flows through nine purpose-built agents that enrich context at every step so the SQL writer receives maximum signal.
User Question
Intent + Context Retrieval
Context Reranker
Query Planner
Query Writer
Validation + Self-Correction
Execution
Results + Visualization
Guided branch
Guided Mode SessionStep-by-step SelectionsQuery Writer
Context-Aware Query Intelligence
Unlike generic text-to-SQL tools, Dialect applies banking semantics and context scoring before the LLM ever generates SQL.
Banking semantic layer
Domain-specific table descriptions and field relationships get injected before SQL generation.
Context reranking
Candidate tables and columns are scored and prioritized before prompt assembly.
Validation + repair
Failed statements feed an automatic correction loop so only executable SQL ships.
Explainable plans
Every query includes planning breadcrumbs so analysts can trust each join and filter.
Query Planning Process
User Question
Show average deposits balances by market for Q4 2025?
Intent → write_sql · aggregation · time_filter12ms
Context → 3 tables, 12 fields · join paths mapped45ms
Planner → GROUP BY market · Filter by quarter · Sort by avg_balance38ms
Validation → All checks passed22ms
Guided Mode — Build Queries Step by Step
Prefer a hands-on approach? Guided Mode walks through six stages of query construction.
Step 01
Select Tables
Browse and choose the banking tables relevant to your question.
Step 02
Select Fields
Pick the exact dimensions and measures for your analysis.
Step 03
Configure Joins
Define relationships with guided join suggestions.
Step 04
Add Filters
Narrow the scope using intuitive filtering controls.
Step 05
Set Sorting
Order results ascending or descending by any selected column.
Step 06
Review & Generate
Preview SQL, execute, and visualize in the same workflow.
Launch Guided Mode
Sessions auto-save. Come back anytime to pick up where you left off.
How We Enrich LLM Context
The secret to accurate SQL is richer context, not just better prompts.
Schema Intelligence
Context Fetcher retrieves schema metadata and relationships, while the Reranker prunes to only the highest-signal tables and fields.
Tables, fields, joins
Structural Planning
Query Planner detects aggregations, grouping, time filters, and sort intent so SQL Writer can execute deterministically.
Patterns, aggregations, filters
Validation & Repair
Generated SQL is checked against live schema. Validation failures trigger automated rewrite and repair before results return.
Schema checks, auto-repair
Frequently Asked Questions
Everything you need to know about querying banking data with natural language.
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Ready to Query Your Banking Data?
Skip the SQL. Ask in plain English, explore with Guided Mode, and get answers from live data in seconds.
- Natural language input mapped to schema-valid SQL
- Explainable plans with table, join, and filter transparency
- Validation and self-correction before query execution