In the world of enterprise analytics, one of the most frustrating bottlenecks is the gap between natural language and structured data. Business users ask questions. Data teams write SQL. Hours (or days) are lost in translation.

At Adhyayan AI, we’ve closed this gap — with precision and speed — using a multi-agent system that collaborates like a team of expert analysts.

The result? A Text-to-SQL system that doesn’t just generate queries — it understands business context, iterates with feedback, and adapts to individual users.

The Problem: SQL is Powerful, But Not Everyone Speaks It

For most users, databases are black boxes. They know what they want:

• “Show me monthly revenue for repeat customers”
• “List orders that were canceled after shipping”
• “Compare average delivery time by region in Q2”

But translating these into accurate, optimized SQL queries still requires technical expertise. And when queries are complex — spanning joins, filters, aggregations, or nested logic — the gap widens further.

Even LLMs struggle when context is deep, schema is large, or business rules are implicit.

The Solution: A Multi-Agent AI System That Works Like a Team

At Adhyayan AI, we didn’t build one monolithic model to solve this. We built an agent army, where each agent is trained for a specific responsibility in the query-generation pipeline.

Here’s how it works:

Step-by-Step Intelligence, Divided Among Specialists

Each agent in our system plays a well-defined role:

• Intent Classification Agent
  Determines if the user wants a summary, comparison, trend, breakdown, etc.
• Query Decomposition Agent
  Splits the user’s question into logical subqueries or CTEs.
• Schema Retriever Agent
  Retrieves relevant tables and columns using vector search + knowledge graph context.
• Join Inference Agent
  Automatically detects the correct joins and data types using schema graph traversal.
• Aggregation & Grouping Agent
  Picks appropriate aggregation functions, identifies GROUP BY logic, and constructs HAVING clauses.
• Temporal & Unit Normalizer
  Handles fuzzy time phrases like “last quarter” or “previous 30 days” and aligns units like INR, USD, %, etc.
• Subquery/Nesting Planner
  Identifies when CTEs or nested logic is required (e.g., Top N queries, ranked comparisons).
• Business Logic Agent
  Injects domain-specific rules (e.g., “VIP customers = orders > ₹50,000/month”) into SQL logic.

Each agent works independently but synchronously, passing structured outputs to the next stage like a well-oiled assembly line.

???? Real Collaboration, Not Just Chained Prompts

Unlike typical LLM pipelines, our agents don’t just stack responses. They collaborate, validate, and self-correct using an internal feedback loop.

• If the Join Agent detects a mismatch in column types, it triggers a schema check.
• If the Aggregation Agent finds missing groupings, it backtracks to update the SELECT clause.
• If the Business Logic Agent flags ambiguity, the system prompts for clarification — all in real time.

This internal feedback loop ensures that the final query isn’t just syntactically correct, but semantically aligned with the user’s intent.

Personalized by Design

Every query isn’t just about “what you said” — it’s about “who you are.”

Our system learns from:

• Your past queries
• Your team or department’s goals
• Your business rules and preferences

Which means two users asking the same question may get slightly different, more personalized SQL — tailored to what they actually meant.

It’s human-like understanding, customized per user, without the wait.

Real Results in Minutes

Our Text-to-SQL system can now:

• Generate complex, multi-join, multi-CTE queries in under 60 seconds
• Adapt to 166+ enterprise-scale schemas
• Learn from user feedback over time and improve accuracy

And because each agent is independently trainable, the system is modular and scalable — we can plug in a new domain, and within hours, it’s generating context-aware SQL for that environment.