Revolutionizing Data Access with Adhyayan AI: Multi-Agent Text-to-SQL Systems for the Modern Enterprise

Introduction: The SQL Bottleneck in Modern Enterprises

Despite the rise of data-driven decision-making, SQL remains a major friction point for many organizations. Non-technical stakeholders depend on analysts to write, debug, and execute complex queries. Even data teams often struggle with navigating unfamiliar schemas, ambiguous business terms, and multi-table joins.

The core challenge is not access to data—but access to querying power.

At Adhyayan AI, we’re building a system that changes this. Our multi-agent Text-to-SQL architecture translates natural language into accurate, optimized SQL queries using a pipeline of expert agents guided by knowledge graphs and semantic understanding.

This is more than a chatbot that generates code—this is a full-fledged intelligent system that understands your data context, business logic, and query intent.

What Is Text-to-SQL and Why Is It Hard?

Text-to-SQL is the process of converting natural language queries (e.g., “What were our top-selling products last quarter in Bihar?”) into valid SQL statements that can be executed against a database.

On the surface, it may seem simple. But real-world Text-to-SQL faces serious challenges:

Business users use vague terms or internal jargon.
Schemas vary wildly across databases and often lack clear naming conventions.
Queries involve joins, aggregations, conditions, subqueries, and nested logic.
Accurate SQL generation requires an understanding of domain semantics, not just syntax.

This is why traditional AI models trained on public datasets (like Spider or WikiSQL) often fail in enterprise settings. They lack context.

Adhyayan AI solves this by introducing agent-based modularity and semantic enrichment.

The Multi-Agent Architecture of Adhyayan AI

Our approach involves breaking down the task of query generation into smaller, manageable steps—each handled by a specialized agent.

Here’s a brief look at how these agents function together:

Intent Classification Agent

Determines the nature of the question—does it involve aggregation, comparison, ranking, or temporal logic? This sets the foundation for how the system proceeds.

Query Decomposition Agent

Complex questions are split into subqueries or logical steps. For instance, if the query involves filtering customers and then aggregating their purchase amounts, this agent builds the logical dependency graph.

Business Terminology Mapper

Maps casual user language to actual table and column names using embeddings and metadata. It can translate “sales” to order_amount or “last month” to a date range.

Schema Retrieval Agent

Pulls relevant tables, columns, data types, and relationships using a vector-based retriever combined with knowledge graph relationships.

Join Inference Agent

Constructs precise join conditions, even across multiple tables, using schema relationships stored in a graph database (e.g., Neo4j). This avoids invalid or ambiguous joins like customer_id = customer_id.

SQL Compiler Agent

Stitches the intermediate outputs into syntactically correct, readable, and optimized SQL. This includes validating column names, aliasing, GROUP BY logic, and more.

Why Knowledge Graphs Matter in SQL Generation

At the heart of Adhyayan AI is a knowledge graph, which captures schema metadata, inter-table relationships, business definitions, and historical usage patterns.

For example, in a retail dataset, the graph might encode:

A customer table connects to orders through customer_id.
product_id in orders maps to products.
category is a valid grouping field for products.
order_date supports time filters and can be compared using DATE() or STRFTIME().

This allows the Join Inference Agent to reason over paths like:

customer → orders → products → category

And determine the correct joins and grouping fields needed to compute a query like:

“Top 3 product categories bought by repeat customers last quarter.”

From Prompt to Production: A Typical Use Case

Let’s walk through an example of how Adhyayan AI helps a marketing analyst.

Query:

"Show me monthly revenue from customers in Patna who ordered more than twice in the last 90 days."

Behind the Scenes:

Intent Classification detects that this is a filtered, grouped aggregation with temporal logic.
Query Decomposition identifies that we need to:
- Filter by location (city = 'Patna')
- Find customers with more than two orders in 90 days
- Aggregate by month
Terminology Mapper matches "revenue" to SUM(order_amount) and “ordered more than twice” to COUNT(order_id) > 2.
Join Inference builds the join path from customers → orders → order_items.
SQL Compiler assembles the query with aliases, correct date logic, and GROUP BY month.

This entire flow happens in seconds.

Key Benefits of Adhyayan’s Multi-Agent SQL Engine

Accuracy Over Guesswork

Every agent validates its outputs based on schema constraints and metadata. No hallucinations. No broken SQL.

Business Context-Aware

Terminology like “churn”, “LTV”, or “repeat customers” is handled through prompt tuning and historical mappings.

Flexible Across Domains

Works across sales, finance, marketing, logistics, and HR datasets with minimal retraining.

Secure by Design

Supports role-based data access, masking sensitive columns, and audit logging.

Deployment and Integration

Adhyayan AI supports multiple modes of deployment:

Web UI for business users and analysts
Slack/Teams bot integration
API endpoints for embedding into dashboards
SQL IDE plug-ins for validation and suggestions

It integrates natively with:

Snowflake
BigQuery
PostgreSQL
MySQL
CSV/Excel ingestion for quick PoC

Future Roadmap

We are actively expanding the platform to include:

Conversational query refinement (follow-up questions)
Semantic caching for faster repeated queries
Version-aware schema adaptation
Integration with data catalogs (e.g., Atlan, Alation)

Our long-term vision is to build a Semantic SQL Operating System for enterprises—where data querying is conversational, transparent, and self-improving over time.

Conclusion: From Data Literacy to Data Fluency

Adhyayan AI enables a leap forward in how enterprises interact with their own data. By combining the interpretability of multi-agent systems with the power of semantic reasoning, we empower data teams to focus on insight—not syntax.

Text-to-SQL is not just a productivity hack. It’s a foundational shift in human-computer interaction for data systems.

Whether you're a data scientist, product analyst, or a business user tired of waiting on queries, Adhyayan AI brings intelligence, speed, and accuracy to your fingertips.

Want to see it in action?

Book a demo
or
Explore Adhyayan AI

Let your data speak—clearly, intelligently, and in perfect SQL.

Blog