Generative AI Chatbot

Generative AI Chatbot

About Client

Industry

Finance

Location

UK

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Project Overview

One of our clients has end customers to whom they provide data analytics services. Our client manages their end-customer data and provides the necessary analysis services for their respective businesses. 

Our client faced challenges with traditional data analysis processes, which involved manual data handling, complex analysis tools, and limited accessibility to insights. 

This approach was not only time-consuming and labor-intensive but also prone to errors and delays due to manual processes and expertise requirements. That too when it comes to managing multiple businesses’ data and providing suitable analytics services to each of them started becoming complex. 

It also involved hustle for the end-customer, as they have to log in to the dashboards and fetch the different reports to see some specific insights. 

In response to these challenges, we introduced a generative AI bot for data analysis. This AI bot leverages natural language processing and machine learning algorithms to understand user queries, extract insights from the data, and provide actionable information in real time. By automating the data analysis process, the AI bot reduces the time and effort required for analysis, improves the accuracy of insights, and enables users to make informed decisions based on data-driven recommendations.

Traditional Data Analysis Process

01
Data Collection: Manually gather data from various sources such as databases, spreadsheets, and external sources.
02
Data Preprocessing: Data is cleaned, transformed, and prepared for analysis, which involves removing inconsistencies, handling missing values, and formatting data.
03
Analysis Tools: Employ complex analysis tools and techniques such as statistical models, regression analysis, and machine learning algorithms to derive insights from the data.
04
Interpretation and Reporting: The analyzed data is interpreted to identify patterns, trends, and relationships, which are then reported to stakeholders for decision-making.

Challenges in Traditional Data Analysis Process

01
Expertise Requirements: Complex analysis tools require expertise in statistics and data science, limiting accessibility to insights for non-technical users.
02
Limited Accessibility to Insights: Users with limited technical expertise may struggle to extract meaningful insights from the data using traditional analysis tools.
03
Lack of Real-time Insights: The delay in data processing and analysis hinders the ability to promptly respond to dynamic business scenarios.
04
Manual Data Handling: The manual collection and preprocessing of data are time-consuming and error-prone.

Our Solution

01
Real-time Insights: The AI bot provides real-time insights and recommendations based on the latest data, enabling timely decision-making.
02
No Tech Expertise Required to Get the Specific Insight: Users can ask the queries/questions in layman’s language. The bot will understand and interpret it and provide a suitable answer/information in return in just one click.
03
Automated Data Processing: The AI bot automates the data collection, preprocessing, and analysis tasks, reducing the manual effort required.
04
Natural Language Interface:Users can interact with the AI bot using natural language queries, making it accessible to users with varying levels of technical expertise.
05
Machine Learning Algorithms: The AI bot uses machine learning algorithms to analyze patterns, trends, and anomalies in the data, enhancing the accuracy of insights.

Outcome

01
Real-time Decision Support: The AI bot provides real-time insights, enabling users to make informed decisions promptly.
02
Time and Cost Savings: The AI bot significantly reduces the time and cost of data analysis by automating manual tasks.
03
Enhanced Accuracy: By leveraging machine learning algorithms, the AI bot improves the accuracy of insights derived from the data.
04
Increased Accessibility: The natural language interface makes data analysis accessible to a wider range of users, including those without technical expertise.
05
Scalability: The AI bot can handle large volumes of data and scale to accommodate growing analytical needs.
06
Benefits in Numbers: As below;

100% boost in ease of accessing the insights

95% improved quality and time of decision-making

90% reduction in delays

80% efficiency increase

75% improved client feedback

Infinite blessings were received from their non-tech users looking for an easy way for their data to speak to them.

Outcome

Appraisals completed a year:

Traditional System - 62,500 (Fees 500 USD - Cost 300 USD)

Remote System - 1,00,000 (Fees 350 USD - Cost 100 USD)

Total appraisers 100

Profit Difference: (Remote) $4,74,50,000/Y - (Traditional)$3,46,75,000/Y = $1,27,75,000/Y

90% reduction in delays

Human error reduced from 7.5% to 1%

Significant environmental benefits, including reduced carbon emissions and sustainability promotion

80% efficiency increase

75% improved client feedback

80% improved quality of appraisals (benefiting lenders/mortgage industry and offering better deals)

100% Boost in Happiness of All the Parties

Infinite blessings Received from the Property Owners and Mortgage Companies

Features of Generative AI Bot for Data Analysis

01
Natural Language Interface: Users can interact with the AI bot using natural language queries.
02
Automated Data Processing: The AI bot automates data collection, preprocessing, and analysis tasks.
03
Real-time Insights: The AI bot provides real-time insights and recommendations based on the latest data.
04
Machine Learning Algorithms:The AI bot uses machine learning algorithms to analyze patterns and trends in the data.

Technologies Used

front-end2
LLM (Large Language Model)

  • OpenAI GPT4 and Amazon Titan
Library

  • Langchain
Vector Database

  • AWS Open Search Serverless Vector DB

Flow Diagram Of The Remote Appraisal Management System