Sales Viesta-AI: An Intelligent AI-Driven Sales Analytics and Decision Support System

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 13 Issue: 04 | Apr 2026 www.irjet.net p-ISSN: 2395-0072

Sales Viesta-AI: An Intelligent AI-Driven Sales Analytics and Decision Support System

Chinmayee Kale¹, Pranali Godase², Ajay Chawda³

¹²Diploma Student, Department of Computer Engineering, Thakur Polytechnic, Mumbai, India

³Assistant Professor, Department of Computer Engineering, Thakur Polytechnic, Mumbai, India

Abstract –

Sales Viesta-AI is an intelligent sales management platform designed to improve business decision-making through data analysis and artificial intelligence. Modern businesses generate large volumes ofsales, customer, andrevenue data, making manual analysis difficult and time-consuming. The proposed system provides a centralized platform for sales tracking, data visualization, predictive analysis, and automatedreporting.

The platform integrates AI-based recommendations, scenario simulation, and incident reporting to support strategic planning and risk analysis. It analyzes historical data to identify trends and generate predictive insights for future business growth. The system also includes locationbased analysis and automated report generation to enhance businessintelligenceandoperationalefficiency.

Overall, Sales Viesta-AI transforms traditional sales data into a smart and data-driven decision support system, helping businesses optimize performance and improve planningthroughintelligentanalytics.

Keywords: Sales Analytics, Artificial Intelligence, Predictive Analysis, Business Intelligence, Decision SupportSystem,DataVisualization.

1. INTRODUCTION

In today’s digital era, businesses generate a large volume of sales data from customers, products, transactions, and revenue activities. Managing and analyzing this data manually is complex and time-consuming, making it difficult for organizations to make accurate and timely businessdecisions.Traditionalsalesmanagementsystems mainly focus on storing data but lack intelligent analysis andpredictivecapabilities.

To address this challenge, artificial intelligence and data analytics technologies are being integrated into business platforms to provide automated insights, forecasting, and decision support. AI-based systems can analyze historical data,identifytrends,predictfuturesalesperformance,and provide strategic recommendations to improve business growthandoperationalefficiency.

Sales Viesta-AI is developed as an intelligent sales management and analytics platform that integrates data visualization, predictive analysis, scenario simulation, and AI-driven recommendations into a single system. The platform helps businesses track sales performance, identifyrisks,generateautomatedreports,andmakedatadrivendecisionsefficiently.

Themaincontributionofthisresearchisthedevelopment of a centralized AI-driven sales intelligence platform that combines predictive analytics, location-based analysis, incident reporting, and scenario simulation to enhance business decision-making and reduce manual effort in salesmanagement.

2. OBJECTIVE

The main objective of Sales Viesta-AI is to develop an intelligent sales analytics platform that improves business decision-making through artificial intelligence anddataanalysis.

•Todevelopacentralizedplatformforsales management,analytics,andreporting.

•Todesignreal-timedashboardsformonitoringsales performanceandtrends.

•ToimplementAI-basedpredictivemodelsforsales forecastingandbusinessanalysis.

•Tointegratescenariosimulationandincident reportingforriskassessmentanddecisionsupport.

•Toprovidelocation-basedanddata-driveninsightsfor businessgrowthandstrategyplanning.

3. LITERATURE REVIEW

Recent research has focused on integrating artificial intelligenceintobusinessintelligenceplatforms.Sharmaet al. (2021) proposed an AI-based sales forecasting system using machine learning techniques to improve business decision-making. Kumar and Singh (2022) developed a predictiveanalyticsframeworkforretailsalesoptimization using data mining and regression models. Zhang et al. (2023) introduced an intelligent CRM system that integrates real-timeanalyticsand automated reporting for business growth. However, these systems lack scenario simulation, incident reporting, and integrated AI Co-Pilot

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 13 Issue: 04 | Apr 2026 www.irjet.net p-ISSN: 2395-0072

support in a unified platform. Sales Viesta-AI addresses these limitations by providing a comprehensive AI-driven salesintelligencesystem.

4. SYSTEM ARCHITECTURE

Sales Viesta-AI follows a layered system architecture that integrates frontend, backend, AI modules, and database components to provide intelligent sales analysis and businessinsights.

Thesystemconsistsofthefollowingmodules:

• User Management Module for authentication and rolebased access

•SalesManagementModule for tracking and storingsales data

• Analysis Module for generating charts and predictive insights

• Reporting Module for automated PDF reports

•AIModuleforrecommendationsandforecasting

The system flow begins with user input through the frontendinterface,whichsendsdatatothebackendserver. ThebackendprocessesthedatausingAImodelsandstores it in the database. The processed data is then visualized through dashboards and reports, providing actionable businessinsights.

4.1 User Interface (Front-End)

The frontend of Sales Viesta-AI is developed using React andTypeScript tocreatea responsiveand interactive user interface. It provides dashboards, charts, and navigation components that allow users to upload data, view analytics,andaccessAI-basedinsightseasily.Reactisused for the main user interface, while Streamlit is used for analyticaldashboardsandvisualization.

4.2 Backend Processing Layer

Thebackendlayerisresponsibleforhandlingthecorelogic and processing operations of the application. It is implemented using Python and utilizes several libraries to perform data processing and predictive analysis. Libraries suchasPandasandNumPy areusedfordatamanipulation and numerical computations, while Scikit-learn supports machinelearningmodelsforpredictivesalesanalysis.

4.3 Database and Data Management Layer

The Sales Viesta-AI system uses a centralized data storage mechanism to manage the sales data efficiently and securely without any alteration. It stores user information, sales records, product

details,reports,andanalyticaldataandstoresitusing databases such as Firebase or MongoDB. It also storesinformationintheformofCSVbasedfiles.This helpsbusinessforreliabledatastorage,easyretrieval, and efficient management of their data. It stores and analysis the data in such a way that can be used for analysis,reporting,insightsandAI-basedpredictions.

4.4 External Technology Integration

To enhance system functionality, the platform integrates several external technologies and services. In addition, Google Generative AI is integrated to provide chatbotbased assistance, enabling users to obtain guidance,AIdrivenrecommendations,ortoaccessriskand its solution. The combination of these external services improves the scalability and reliability of the platform while enabling realtime decision-making. Also for the location-based analysis it is implemented using mapping libraries such as Folium/PyDeck along with Geopy for geolocation processing and GeoJSON data for regional visualization.

5. FEATURES OF Sales Viesta-AI

TheSalesViesta-AIplatformdeliversmanyfunctionalities which aims to improve the sales management, data analysis,anddecision-making.Thesystemfacilitatesusers to manage sales data efficiently, analyze sales performance,andprovidevisualinsights.Thekeymodules are Sales Management, Analytics, Reporting, AI-growth intelligence, and Data Import. These features allow userstotracksalesactivities,monitortrends,andenhance salesperformance.By integratingthesecapabilities,theapplication assists users make correct decisions, optimize sales strategies, and improve overall business efficiency. The platform ensures that users can access important insights quickly, respond to market changes effectively, and maintain better control over their sales operations. The location-based analysis it has been implemented by mapping libraries such as Folium/PyDeck along with Geopy for geolocation processingandGeoJSONdataforregionalvisualization.

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 13 Issue: 04 | Apr 2026 www.irjet.net p-ISSN: 2395-0072

5.1 AI-Growth intelligence

TheAI-Growthintelligencefeatureprovidesreal-timedata filteringfrom(1MtoMAX).Thesalesgraphinthisfeature toggles between parameters such as Price and PE Ratios, etc to highlight the shifts in the sales, rating, customer behaviour, retention, revenue growth etc. It also allows to zoom and view the exact values or the growth retention matrix at which the sales dropped. It converts basic data into actionable visual insight. It includes following features:

• Dynamic Time-Range Filtering: Fully functional 1M, 3M, 6M, 10M, MAX buttons that transforms the data to show specific historical history based ontheselectedperiod.

• Toggling: Easily switch between Revenue and Profittrends

• views while maintaining consistent trend line overlays.

• Trend Tracking: Real-time calculation and rendering of 50-day and 200-day Moving averages. This feature uses a polynomial regression technique. It categorizes the data into risks, strengths, strategic recommendations, and explains how risk factors impact performance of the product or sales. This feature helps the stakeholders to make quick and accurate decisionswithoutmanualcalculations.

5.2 AI Co-Pilot

The AI Co-Pilot feature in Sales Viesta-AI acts as an intelligentassistantthathelpsusersmakebetterbusiness decisions. It analyzes historical sales data and provides smart suggestions, such as identifying top-performing products, predicting future sales trends, and recommending pricing strategies. This feature simplifies complexdataanalysisbypresentinginsightsinaneasy-tounderstand format. It also helps users by answering queries, highlighting important patterns, and guiding users in optimizing sales performance, thereby improving efficiency and decisionmaking. Here an important feature thatislocation-basedanalysisithasbeenimplementedby mapping libraries such as Folium/PyDeck along with Geopy for geolocation processing and GeoJSON data for regional visualization, it shows which area is performing wellandwhichisnot.Italsoshowsareawisegrowth.

5.3 Data Import

TheData Importfeatureallowsusersto uploadsalesdata primarily in CSV format. After uploading, user can view data analysis results. It also generates the report. It combinesreal-timeanalyticswithAI-generatedinsightsto provide a combined view of sales performance, including revenue trends, conversion rates, and churn analysis. The system supports secure report generation and structured

datastoragetomaintaindataintegrityandreliability. This feature simplifies reporting, saves time, and helps businessesmakedecisionsefficiently

5.4 Incident Reporting

The Incident Reporting feature in Sales Viesta-AI helps businesses identify and manage unexpected events that may affect sales performance or operations, such as sudden sales drops, product shortages, pricing issues, or regionalperformancedecline.Thisfeatureallowsusersto record incidents by selecting relevant parameters like product category, location, and time period, enabling structuredtrackingofbusinessissues.

Once an incident is reported, the system analyzes historicalandcurrentsalesdatatoidentifypossiblecauses and patterns. It also provides AI-based recommendations and risk analysis to support faster and more accurate decision making. The platform displays incident trends and severity levels through dashboards and reports, helping businesses monitor problems and take corrective actionsinatimelymanner.

Overall, the Incident Reporting module improves risk management by providing a systematic and data-driven approachtoidentifyingandresolvingbusinesschallenges, ensuringbetteroperationalcontrolandstrategicplanning.

5.5 Information

The Information module in Sales Viesta-AI provides users with structured resources and business insights to stay informed, prepared, and proactive in managing sales operations and decision-making. It organizes important sales data, product details, performance metrics, and businessinsights ina centralizedplatform,allowingusers to easily access relevant information and monitor overall business growth. The module ensures transparency, improves understanding of business performance, and supportsefficientdecision-makingbypresentingdataina clearandstructuredmanner.Themoduleincludesseveral keycomponents:

• Sales Information: Provides detailed insights aboutsalesperformance,includingproduct-wiserevenue, profit,andgrowthtrends,helping users monitorbusiness performanceeffectively.

• Product Information: Displays product-related details such as category, pricing, demand, and performance, enabling users to identify top-performing andlow-performingproducts.

• Customer and Revenue Insights: Presents structureddataoncustomerbehavior,revenuegeneration, and market trends, allowing businesses to understand theirtargetaudienceandimprovestrategies.

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 13 Issue: 04 | Apr 2026 www.irjet.net p-ISSN: 2395-0072

• Reports and Analytics Information: Offers summarized reports, charts, and visual dashboards that convert complex sales data into understandable insights, supportingfasterandaccuratedecision-making.

• System Updates and Feedback: Allows users to reviewsystem-generatedinsightsandprovidefeedbackor updates, ensuring continuous improvement and better platformperformance.

6. TECHNOLOGIES USED

6.1

Programming and Framework

The backend of Sales Viesta-AI is implemented using Node.js and Express.js for API handling and system communication, while Python is used for AI processing anddataanalysismodules.

React and TypeScript: Used to design a responsive and interactiveuserinterface,enablingsmoothnavigationand efficientfront-enddevelopment. Node.js and Express.js: Handle backend operations, API routing, and server-side logic to ensure seamless communication between the client and system. Streamlit: Used to build interactive dashboards and visual analytics forbetterunderstandingofsalesdataandinsights.

6.2

Database and Security

The Sales Viesta-AI platform uses a centralized database system to store user information, sales records, product data, and analytical reports in a secure and organized manner. Technologies such as MongoDB or Firebase are used for efficient data storage and retrieval, while CSVbased storage supports structured data analysis and reporting. The system includes secure authentication and role-based access control to protect user accounts and ensure authorized access to the platform. Proper data handling and security mechanisms are implemented to maintain data privacy, reliability, and overall system integrity.

6.3 System Utilities

The Sales Viesta-AI platform uses various system utilities to support data processing, visualization, and overall system performance. Mapping and geolocation utilities such as Folium, PyDeck, and Geopy are used to provide locationbased sales analysis and regional visualization. Reporting utilities are used to generate PDF reports and analytical summaries for better decision-making. Additional libraries and tools support data visualization, chartgeneration,andsystemintegration,ensuringsmooth functioning of the platform and efficient handling of analyticalandoperationaltasks.

6.4 Data Processing and Analysis

The Sales Viesta-AI platform uses data processing and analysis techniques to transform raw sales data into meaningful insights. Libraries such as Pandas and NumPy are used for data cleaning, preprocessing, and numerical computations, while Scikit-learn supports predictive analysis and trend identification. The system analyzes historical sales data to detect patterns, calculate growth metrics, and generate forecasts, enabling businesses to makeinformedanddata-drivendecisions.Thisprocessing ensures accurate analysis, efficient handling of large datasets,andreliablebusinessintelligenceoutputs.

6.5 AI and External Integration

The Sales Viesta-AI platform integrates artificial intelligence and external technologies to enhance decision-making and system intelligence. Google Generative AI is used to provide AI Co-Pilot assistance, enabling users to receive automated insights, recommendations, and query-based responses related to sales data and business performance. Predictive analysis models analyze historical data to identify trends and generate forecasts, while scenario simulation helps users evaluate risk, profit, and estimated revenue before implementingbusinessdecisions.Theintegrationofthese AI and external technologies improves system efficiency, supports real-time decision-making, and provides intelligent business guidance through automated analysis andrecommendations.

“This integration ensures scalability, intelligent automation, and enhanced business intelligence capabilitieswithintheSalesViesta-AIplatform.”

7. METHODOLOGY

7.1

Data Collection

The Sales Viesta-AI system begins with data collection, where users upload sales datasets in CSV format or enter business-relateddataintotheplatform.Thecollecteddata includes product details, pricing, revenue, category, location,andperformancemetrics.Thisdata isstoredina centralized database and used for further analysis and processing.Thestructureddatacollectionprocessensures accuracy, consistency, and efficient handling of business informationforintelligentdecision-making.

7.2 Data Preprocessing

After data collection, the system performs data preprocessingtopreparethedatasetforanalysis.Thisstep includes data cleaning, handling missing values, formatting,andorganizingthedataintoastructuredform. Libraries such as Pandas and NumPy are used to transform raw sales data into meaningful and usable information.Properpreprocessingensuresthatthedatais reliableandreadyforpredictiveanalysisandvisualization.

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 13 Issue: 04 | Apr 2026 www.irjet.net p-ISSN: 2395-0072

7.3 Data Analysis and Prediction

In this stage, the system analyzes historical sales data to identify trends and patterns. Techniques such as polynomial regression and moving average calculations are used to forecast future sales performance and detect growthordecline in producttrends. Theanalysishelps in identifying risks, strengths, and business opportunities, enablinguserstomakedata-drivendecisionsandimprove overallsalesstrategies.

7.4 AI Co-Pilot and Recommendation Engine

The AI Co-Pilot module provides intelligent recommendations and automated insights based on analyzed data. It uses AI-based models and Google Generative AI to understand user queries and generate meaningful business suggestions. The system provides recommendations such as pricing strategies, product performance insights, and sales improvement strategies, helping businesses make faster and more accurate decisions.

7.5 Scenario Simulation and Incident Analysis

The methodology also includes scenario simulation and incidentanalysistoevaluatebusinessrisksandunexpected events. Users can test different business conditions to estimate profit, revenue, and potential risks before implementing strategies. The incident analysis feature identifiesunusualsalespatternsorperformancedropsand provides recommendations to resolve them, ensuring betterriskmanagementandoperationalstability.

7.6 Visualization and Report Generation

Finally, the system presents the analyzed data through dashboards,charts,graphs,andPDFreports.Visualization tools help users easily understand sales performance, trends, and predictions. The generated reports provide clear and actionable insights that support business planning and decision-making. This step ensures that complexdataisconvertedintosimpleandunderstandable informationforeffectivemanagement.

8. IMPLEMENTATION

8.1

Frontend Implementation

The frontend of Sales Viesta-AI is developed using React and TypeScript to create a responsive and user-friendly interface. The system includes various UI components suchasdashboards, navigation bars, featuresections, and data visualization panels that allow users to interact with the platform easily. The frontend is designed to provide smoothnavigation,cleardatapresentation,andinteractive charts,ensuringthatuserscanuploaddata,viewanalytics, and access AI-based insights efficiently. Modern styling frameworks and component-based architecture are used

to enhance user experience and maintain system consistency.

8.2 Backend Implementation

The backend of Sales Viesta-AI is developed using Node.js and Express.js to manage server-side operations and system functionality. It handles API routing, data processing, user authentication, and communication between the frontend and database. The backend ensures secure data transfer, efficient request handling, and smoothsystemperformance.Italsomanagesdatastorage, report generation, and integration with AI modules, enablingreal-timeanalysisandreliablesystemoperations.

8.3 AI Module Implementation

The AI module of Sales Viesta-AI is implemented using Python and machine learning libraries to provide intelligent analysis and predictive insights. Libraries such asPandas and NumPy are used for data processing, while Scikit-learn supports predictive modeling and trend analysis. The system applies polynomial regression and moving average techniques to forecast sales performance and identify patterns in historical data. Google Generative AI is integrated to enable AI Co-Pilot functionality, which provides automated recommendations, business insights, and querybased assistance, enhancing decision-making andsystemintelligence.

8.4 Database Implementation

The database of Sales Viesta-AI is designed to store and manage sales data, user information, product details, and analytical reports in a secure and structured manner. Technologies such as MongoDB or Firebase are used for centralized data storage and efficient data retrieval, while CSV-basedstoragesupportsdatasetuploadandprocessing for analysis. The database ensures proper organization of business data, secure access control, and reliable storage, allowing the system to perform accurate analysis and generatemeaningfulinsights.

8.5 System Integration and Deployment

TheSalesViesta-AIplatformintegratesfrontend,backend, AI modules, and database components to create a unified andefficientsystem.Thefrontendcommunicateswiththe backend through API requests, while the backend processes data and interacts with AI modules and the database to generate insights and reports. External technologies such as Google Generative AI, Streamlit dashboards, and mapping utilities are integrated to enhance system functionality. The complete system is designed to ensure smooth deployment, scalability, and efficient performance, enabling users to access real-time analytics, AI recommendations, and business insights throughasingleplatform.

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9 Advantages of Sales Viesta-AI

Sales Viesta-AI provides multiple advantages that help businesses improve their salesperformance and decisionmaking.Ituses AI-based decision making toanalyzedata and guide smarter business strategies, while real-time sales analysis allows continuous monitoring of sales activities. The system supports scenario simulation to predict future outcomes and includes incident reporting to quickly detect and resolve issues. With centralized data management, all sales information is stored in one place, and location-based analysis helps understand regional performance.Italsooffers automated reporting to reduce manual work and a user-friendly interface for easyoperation.Overall,SalesViesta-AIenhances business intelligence by providing clear insights that support growthandbetterplanning.

10. RESULTS AND DISCUSSION

10.1 System Output Overview

The Sales Viesta-AI platform was successfully developed and tested to analyze sales data and generate business insights. The system processes uploaded datasets and providesanalyticaloutputssuchassalestrends,predictive analysis, scenario simulation, and incident reporting. The integrationoffrontend,backend,AImodules,anddatabase ensuressmoothfunctionalityandreliabledataprocessing. The results show that the platform can transform raw

sales data into useful business intelligence and support decision-making

10.2DashboardandVisualizationResults

The dashboard provides basic visualization of sales data through charts and graphical representations. Users can view product performance, sales trends, and dataset summaries after uploading data into the system. The visualization helps in understanding overall business performance and identifying important patterns in sales data. Although the current dashboard includes essential visualcomponents,furtherimprovementscanbemadeby adding advanced analytics and real-time monitoring features.

10.3 AI Growth Intelligence Results

The AI Growth Intelligence module analyzes historical sales data and generates predictive insights for future performance. The system uses machine learning techniques to estimate growth trends and provide recommendations for improving sales strategies. The results indicate that AI-based analysis helps users understand market behavior, identify growth opportunities,andmakebetterbusinessdecisions. The AI Co-Pilotfeaturealsoassists usersbyprovidingautomated suggestionsandinsightsbasedonthedata.

10.4 Scenario Simulation Results

The scenario simulation module allows users to test different business conditions and analyze possible outcomes. By adjusting parameters such as product demand, pricing, and investment, the system estimates profit, revenue, and risk levels. The results show that this module helps in evaluating business strategies before implementationandsupportsstrategicplanning.

10.5 Incident Reporting Results

The incident reporting module identifies performance issues such as sudden sales drops or operational risks in the dataset. The system generates alerts and analytical summaries to help users take corrective actions. The results demonstrate that incident monitoring improves system awareness and supports better business management.

10.6 Performance and System Evaluation

The performance of Sales Viesta-AI was evaluated based on functionality, usability, and analytical capability. Thesystemprovidesefficientdataprocessing,simpleuser interaction, and reliable analytical output. The integration ofAIanddataanalysisimprovesbusinessintelligenceand reducesmanualeffortinsalesanalysis.Overall,thesystem performs effectively in providing insights and supporting businessdecision-making.

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 13 Issue: 04 | Apr 2026 www.irjet.net p-ISSN: 2395-0072

11. LIMITATIONS OF THE SYSTEM

TheSalesViesta-AIplatformprovidesusefulanalyticaland AI-basedbusinessinsights;however,thecurrentversionof the system has certain limitations that need to be addressed in future development. The system mainly depends on the quality and accuracy of the uploaded dataset, and incorrect or incomplete data may lead to inaccurate analysis and predictions. The dashboard and visualization features are currently basic and do not support advanced real-time monitoring or dynamic filtering.

The AI prediction model is based on limited machine learning techniques and may not provide highly accurate forecasts for complex or large-scale business environments. The system also requires internet connectivityandpropersystemresourcestorunsmoothly, which may affect performance on low-end devices. Additionally,theplatformisstillintheprototypestageand does not support full enterprise-level scalability or integrationwithlivebusinessdatabasesandAPIs.

Despite these limitations, the system provides a strong foundation for AI-based sales analysis and can be improved further through advanced AI models, real-time dataintegration,andenhanceduserinterfacefeatures.

12. FUTURE SCOPE

The SalesViesta-AIplatformprovidesa strongfoundation for intelligent sales analysis and business decisionmaking; however, several enhancements can be implementedinthefuturetoimproveitsperformanceand scalability. The system can be upgraded by integrating advanced machine learning and deep learning models to provide more accurate sales forecasting and predictive analysis.Real-timedataintegrationwithexternalAPIsand livebusinessdatabasescanbeaddedtoenablecontinuous monitoringofsalesperformanceandmarkettrends.

In the future, the platform can be deployed on cloud infrastructure to improve scalability, security, and accessibility for large-scale business environments. A mobile application version of Sales Viesta-AI can also be developedtoallowuserstomonitorsalesandanalyticson smartphones and tablets. Additionally, advanced dashboard features such as dynamic filters, automated reporting, and real-time alerts can enhance user experienceanddecision-makingcapabilities.

The system can also be expanded by integrating advanced AI chatbots, automated marketing recommendations, and business strategy generators to support organizations in planning and execution. Overall, future improvements will focus on making the platform more intelligent, scalable, and efficient, transforming it intoacompleteAI-drivenbusinessintelligencesolution.

13. CONCLUSION

The Sales Viesta-AI platform was developed to provide an intelligent and efficient solution for sales management,

data analysis, and business decision-making. The system successfully integrates data processing, visualization, AIbased analysis,and reporting features to transform raw sales data into meaningful insights. It helps businesses monitor performance, identify growth opportunities, analyze risks, and make strategic decisions with reduced manualeffort.

The platform demonstrates how artificial intelligence and data analytics can improve traditional sales management systems by providing predictive insights, scenario simulation, and automated recommendations. Although the current system has some limitations, it provides a strong foundation for future improvements such as realtime analytics, advanced AI models, and cloud-based deployment. Overall, Sales Viesta-AI proves to be a useful andscalablesolutionformodernbusinessintelligenceand sales analytics, supporting organizations in achieving betterperformanceanddatadrivendecision-making.

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Volume: 13 Issue: 04 | Apr 2026 www.irjet.net p-ISSN: 2395-0072

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