Building IntelliQuery: An AI-Powered Data Analyst

In the world of data, there is often a barrier between the people who need the data (business analysts, managers) and the data itself. That barrier is usually a complex query language like SQL or MongoDB Aggregations.

I built IntelliQuery to break down this barrier. It is an AI-powered platform that allows users to ask questions in plain English and get accurate data back from a MongoDB database.

Here is a deep dive into how I engineered it, the challenges I faced, and the solutions I implemented.

The Core Problem

Large Language Models (LLMs) are great at writing code, but they aren't perfect. When you ask an LLM to "Find the total sales for last month," it might generate a MongoDB query that looks correct but fails to execute due to a syntax error or a hallucinated field name.

In a production environment, a "fairly good" query isn't enough. It needs to be executable and accurate.

The Solution: A Self-Correcting Pipeline

To solve this, I didn't just connect an LLM to the database. I architected a Self-Correcting RAG (Retrieval-Augmented Generation) Pipeline using LangGraph.

How it works:

1. Generation: The system generates an initial MongoDB aggregation query based on the user's prompt.
2. Validation: It attempts to execute this query against the database schema.
3. Autonomous Repair: If the query fails (e.g., syntax error), the system catches the error. Instead of crashing, it feeds the error message back into the LLM, effectively saying, "You made a mistake here, please fix it."
4. Execution: This loop continues until a valid, executable query is produced.

This "agentic" workflow allows the system to repair itself at runtime without any user intervention.

optimizing for Cost and Performance

One of the biggest challenges with AI applications is the cost of embeddings. Sending schema data to OpenAI or Gemini for every single interaction can get expensive quickly.

I engineered a cost-efficient semantic search engine by deploying local Transformer models.

• Local Embeddings: Instead of paying for external API calls, the system generates vector embeddings for the database schema locally.
• Zero Latency: This eliminated external network hops for embedding generation, making the search faster and removing API costs entirely for this part of the stack.

Security First

Giving an AI access to a database carries significant risk. I implemented a strict Security-First Architecture to ensure data safety:

• NoSQL Injection Prevention: I built strict prompt guardrails that sanitize inputs before they ever reach the query generation stage.
• Encryption: The multi-tenant backend uses AES-256-GCM encryption to secure sensitive credentials.
• Isolation: Dynamic connection pooling ensures that data between different tenants remains strictly isolated.

Tech Stack

• Frontend: React, Tailwind CSS, shadcn/ui
• Backend: Node.js, Express
• AI & Logic: LangChain.js, LangGraph, Gemini API
• Database: MongoDB (with Atlas Vector Search)

Conclusion

IntelliQuery was more than just a wrapper around an API. It was an exercise in building robust, agentic systems. By combining local AI models with a self-healing architecture, I was able to create a tool that is not only powerful but also reliable and cost-effective.

It demonstrates that the future of software isn't just about AI writing code—it's about AI fixing its own mistakes to deliver a seamless user experience.