Case Study

LLM-Based Data Analytics

Category:

AI Research & Data Analytics

Impact:

12 Weeks | MVP in 60 Days

Background

Organizations face challenges in extracting insights from large, unstructured datasets. Traditional business intelligence tools often require technical expertise and manual querying, which slows down decision-making. It was envisioned as an AI-powered analytics assistant that allows users to ask natural language questions and instantly generate insights, SQL queries, and visual reports. A robust, scalable, and production-ready architecture was required to bridge human interaction with advanced LLM-based data processing.

Project Goals

Build a text-to-SQL system for natural language querying of databases
Enable dynamic, smart visualizations generated directly from user questions
Design and deploy a RAG (Retrieval-Augmented Generation) system to enhance accuracy with contextual retrieval
Research zero-shot text-to-speech for voice-enabled analytics
Deliver a production-ready backend architecture to support enterprise scalability
Launch an MVP rapidly to secure accelerator enrollment and funding opportunities

Our Approach

Text-to-SQL & Visualization Pipelines

Developed natural language → SQL pipelines enabling non-technical users to query data effortlessly. Integrated automated visualization tools to display insights in real-time dashboards.

Retrieval-Augmented Generation (RAG)

Designed and deployed a production-grade RAG pipeline for combining document retrieval with LLM inference. Improved contextual accuracy, reducing hallucinations and increasing trust in AI-driven analytics.

Speech & Accessibility

Researched zero-shot text-to-speech models to enhance user experience with voice-based analytics.

Backend & Deployment

Built a scalable AWS-based backend, optimized for data-intensive workloads. Deployed APIs to enable seamless integration with frontend and enterprise systems.

Agile MVP Delivery

Delivered a functional MVP in 60 days, accelerating product-market validation. Supported successful enrollment of EKAI into the C10 Labs accelerator program.

Key Results

Fully functional LLM-powered analytics assistant built from concept to MVP in 60 days
Automated Text-to-SQL + Visualization pipelines for natural language queries
Deployed production-ready RAG system improving answer reliability
Advanced research in zero-shot text-to-speech integration
Scalable, AWS-based backend architecture for long-term growth
Enabled C10 Labs accelerator admission, supporting startup scaling