AI Chatbot Excellence

This AI Chatbot was developed to simplify job data analytics by providing a conversational interface connected to a dedicated analytics database. Instead of relying on manual SQL queries or static reports, users can now ask natural language questions such as “How many tyres fitted in the last 14 days?” or “Show call outs in 2024 month wise” and receive instant insights. The system dynamically translates queries into SQL, fetches results from the analytics database, and presents answers in text, tables, or interactive graphs. With automated ETL pipelines ensuring daily data updates, the chatbot empowers both technical and non-technical users to access accurate, real-time analytics. The system is built for scalability, compliance, and user-friendliness, giving business teams a powerful tool for faster and more informed decision-making.

+

image

Key Challenges and Solutions

Complex Database Structure

  • The job database was highly normalized and difficult for analytics. A denormalized OLAP database was created using ETL for simplified querying.

Lack of SQL Knowledge

  • Users couldn’t write SQL queries to access insights. The chatbot converted natural language queries into SQL automatically.

Large Data Volumes

  • Extracting huge datasets caused slow performance. Dask was implemented for fast, parallel data processing.

Data Quality Issues

  • Duplicate and inconsistent records affected accuracy. Python scripts validated and cleaned data before loading.

Need for Visual Insights

  • Raw data outputs weren’t enough for decision-making. The chatbot generated charts and graphs for clear visualization.

Overview

The AI chatbot serves as an intelligent conversational analyst that empowers users to interact with complex job data through simple natural language. Instead of writing SQL queries, users can ask questions like “How many tyres fitted in the last 14 days?” or “Jobs done today” and instantly receive accurate results in the form of summaries, tables, or graphs. It not only provides numerical insights, such as costs exceeding £2.4M in a single day, but also generates visual outputs like pie charts for monthly call outs or detailed reports for customer-specific performance. By bridging the gap between raw data and business decision-making, the chatbot delivers real-time, reliable, and user-friendly analytics accessible to everyone in the organization.

Icon

Industry

Helthcare

Icon

Region

United States

Technical Stack

image
image
image
image
image

Our Role

Icon

Development

Icon

Deployment on server

Project Highlights

  • Natural Language to SQL
    Chatbot converts plain English into optimized SQL queries.
  • Daily ETL Refresh
    Ensures the analytics DB always contains the latest job data.
  • Graphical Analytics
    Auto-generated charts like pie charts for monthly inspection ratios.
  • Real-Time Job Tracking
    “Jobs done today” query shows daily live updates.
  • Customer-Specific Reporting
    Provides customer-level insights (e.g., tyres for company).
  • Error-Handled Responses
    Graceful fallback messages when queries fail.
  • Concurrent Session Support
    Handles 300+ users querying at the same time.
  • Domain Vocabulary Recognition
    Interprets terms like “call outs” and “tyres fitted.”
  • Data Privacy First
    Only schema shared with AI model; data remains secure.
  • Future Expandability
    Easy integration with CRM/ERP data sources for broader analytics.
image

Project Screenshots

Get a Free Consultation Today!