Business Data Insight Bot

Excel bot-1

About Client

Industry

Finance

Location

Canada

Share Now
Facebook Linkedin Link
Project Overview
Traditional Process
Challenges
Our Solution
Outcome ​
Features
Technologies Used
Demo Video

Project Overview

This Business Data Insight Tool entails creating and deploying a robust Chatbot, designed to facilitate seamless querying of diverse datasets. The primary objective of this is to empower users with real-time access to comprehensive financial and non-financial information. By performing a myriad of operations on historical and current data, users can extract valuable insights into past and present performance metrics.

The chatbot’s capabilities extend to querying various aspects such as past financial records, non-financial data, current-year performance, sales comparisons between the current and previous years, and customer engagement trends. Users can pose a wide array of questions related to historical and present data, leveraging the tool to gain a holistic understanding of their business dynamics.

Behind the scenes, the system iteratively conducts multiple operations on the stored data, providing users with visually representative outputs that correspond to their queries. The ultimate goal is to augment operational efficiency, enhance financial planning processes, and empower businesses to make well-informed, data-driven decisions. By offering a user-friendly interface and facilitating a deep dive into historical and current data trends, the Business Data Insight Chatbot Tool aims to contribute significantly to sustained growth and competitiveness for businesses utilizing this valuable resource.

Traditional Process

01
Data Retrieval Process: Before the chatbot implementation, accessing information from Excel sheets was burdensome. Users had to manually sift through extensive files, like financial data, which was time-consuming. This inefficient process often led to delays in decision-making and decreased productivity.

Challenges Faced

01
Manual Data Processing: Before implementing the chatbot, users had to rely on manual methods to analyze the data, which can be time-consuming and error-prone.
02
Complex Data Queries: Users may struggle with formulating complex queries or extracting specific insights from the datasets, leading to inefficiency and frustration.
03
Data Accuracy and Consistency: Ensuring the accuracy and consistency of processed data is crucial for reliable decision-making. Inaccurate or inconsistent data could lead to erroneous insights.

Our Solution

01
Automated Data Processing: Integrate the chatbot with automated data processing capabilities to eliminate manual analysis and provide instant, accurate insights.
02
Natural Language Processing: When users interact with the chatbot, they naturally express their queries using natural language. The chatbot is designed to comprehend these natural language inputs and respond accordingly, ensuring seamless communication and providing effective guidance.
03
Data Validation: Implement data validation checks to verify the accuracy and consistency of input data, along with algorithms to detect anomalies and provide corrective suggestions.
04
Admin Panel: The admin can upload multiple Excel simultaneously and select one to retrieve answers from.

Outcome

Achieving an 80% reduction in data retrieval time, improving operational efficiency and decision-making processes.

Resulting in a 40% decrease in resource allocation for data analysis tasks, leading to cost savings in analytical workflows.

Facilitating a 20% increase in revenue through quick sales comparisons and customer engagement trend analysis, enabling users to identify and capitalize on sales opportunities.

Empowering businesses with data-driven decisions, resulting in a 25% increase in strategic decision-making effectiveness.

Features

Multiple Excel file upload

Conversational history

Technology Stack

LLM
Large Language Models (LLMs)

  • SQLCoder 34B using Hugging Face Transformers Library

Front-end

  • React

Backend

  • Node
  • Python
  • Flask

chatbot
Bot Framework

  • Botpress V12

sorting
Storing the data for the RAGs

  • ChromaDB(Vector DB)

Database

  • SQL Database (PostgreSQL)

Project management
Project Management

  • Azure DevOps

data visulazation
Data Visualization

  • D3.js or Plotly

Deployment and Scaling

  • Docke
  • Kubernetes

Cloud Services

  • AWS Cloud Services (EC2, S3, ACM, Route53, Simple Email Service, WAF, Nginx)

Demo Video