Proactive agent-based AI system to optimize customer experience in real time on enterprise platforms

Abstract

A proactive, agent-based AI system (100) for optimizing the customer experience in enterprise platforms in real time, comprising: (a) a real-time customer data input module configured to continuously collect, normalise and label structured and unstructured data from multiple enterprise sources, including CRM, ERP, web interfaces, mobile applications and customer service platforms; (b) a context-aware analytics and sentiment detection module adapted to perform real-time analysis of incoming data streams using natural language processing, emotion detection and contextual intent classification to identify customer experience patterns and sentiment indicators; c) an Autonomous Agentic Decision-Making Engine comprising a reinforcement learning-based multi-agent framework configured to evaluate detected experience contexts and autonomously initiate optimal intervention strategies based on predefined customer experience key performance indicators (KPIs); d) a Customer Journey Mapping and Prediction Module configured to create dynamic behavioral profiles, generate real-time journey maps, and predict future customer actions or churn risks based on historical and current data; (e) a Personalized Experience Orchestration Module connected to multiple customer-facing channels and configured to deliver contextually relevant, personalized content, offers or services based on agent decisions; (f) a feedback loop and continuous learning module designed to collect outcome data from implemented interventions and continuously update the learning parameters of the agent-based machine to improve future decisions; and g) a security, compliance and ethical governance module configured to enforce data protection, audit logging, consent management and regulatory compliance in enterprises, with all modules interconnected to enable autonomous, adaptive and real-time optimization of the customer experience across the entire enterprise platform.

Description

[0001] The present invention relates to the field of artificial intelligence and enterprise software systems. In particular, it relates to agent-driven AI systems that autonomously optimize the customer experience (CX) in real time. The invention integrates intelligent agents into enterprise platforms to proactively improve user satisfaction and retention.

[0002] In today's dynamic business environments, delivering consistent and satisfying customer experiences across multiple digital touchpoints remains a significant challenge. Traditional CRM and customer service systems are largely reactive, responding to customer inquiries or issues only after they occur. This delay can lead to customer dissatisfaction, missed business opportunities, and reduced brand loyalty. Companies are increasingly looking for intelligent systems that can anticipate customer needs and respond proactively rather than reactively.

[0003] Traditional AI tools in enterprise platforms often rely on static data models, predefined workflows, and periodic data analyses that are insufficient to capture real-time behavioral changes or contextual engagement. These systems are unable to quickly adapt to shifts in customer sentiment, intent, or service expectations. Furthermore, siloed data systems across departments prevent a unified view of the customer, hindering decision-making and personalization. As a result, companies struggle to create timely, meaningful, and relevant customer interactions that increase value and satisfaction. To address these limitations, there is a critical need for a proactive, agent-based AI system that continuously learns, adapts, and autonomously takes action to optimize the customer experience in real time.Such a system must leverage streaming data, predictive analytics, and autonomous agents to identify customer issues, initiate context-dependent interventions, and enable hyper-personalized interactions across enterprise platforms. This invention solves this problem by introducing an intelligent, proactive, and self-improving CX optimization system that transforms enterprise customer interactions from reactive to predictive and value-driven.

[0004] One goal of this disclosure is to enable autonomous optimization of the customer experience in real time using agent-based AI.

[0005] Another objective of this disclosure is to reduce customer churn by proactively identifying and addressing dissatisfaction.

[0006] Another objective of this disclosure is to improve personalization across all of the company's digital touchpoints.

[0007] Another goal of this disclosure is to continuously learn and improve through adaptive feedback loops.

[0008] Another objective of this disclosure is to increase operational efficiency by automating CX decision making

[0009] Another objective of this disclosure is to provide predictive insights into the customer journey and behavioral predictions.

[0010] Another objective of this disclosure is to ensure data protection, compliance and ethical AI governance.

[0011] Another objective of this disclosure is integration into existing corporate systems and platforms.

[0012] Further objects and advantages of the present disclosure will become apparent from the following description, which is not intended to limit the scope of the present disclosure.

[0013] The present invention relates to a proactive, agent-based AI system that autonomously optimizes the customer experience (CX) in real time across enterprise platforms using intelligent digital agents. It transforms traditional reactive systems into predictive, action-oriented architectures.

[0014] Another embodiment of the present invention is a real-time customer data ingestion module that collects and normalizes structured and unstructured data from various sources such as CRM, ERP, web, mobile, and customer support channels.

[0015] Another embodiment of the present invention is the contextual analysis and sentiment detection module, which applies advanced NLP and emotion detection to detect intent, dissatisfaction, or opportunities to engage during customer interaction.

[0016] Another embodiment of the present invention is the Autonomous Agentic Decision-Making Engine, which uses reinforcement learning to enable AI agents to take independent and real-time actions to improve KPIs such as satisfaction, loyalty, and conversion.

[0017] Another embodiment of the present invention is the Customer Journey Mapping and Prediction Module, which dynamically creates real-time profiles of customer behavior and predicts future actions or risks.

[0018] Another embodiment of the present invention is the Personalized Experience Orchestration module, which enables the system to deliver AI-recommended interventions through digital touchpoints such as websites, emails, mobile apps, and chat interfaces.

[0019] Another embodiment of the present invention is that the feedback loop and continuous learning module captures the results of each AI-controlled action and feeds performance metrics back into the learning engine.

[0020] Another embodiment of the present invention is that the system includes a security, compliance, and ethical governance module that protects user data, ensures regulatory compliance, and enforces responsible AI behavior.

[0021] The present invention relates to a proactive AI system (100) for real-time customer experience optimization in enterprise platforms with a modular architecture that enables intelligent, autonomous, and continuous improvement of customer interactions. The invention integrates multiple AI-driven modules that work together to monitor, analyze, and optimize CX across digital channels within enterprise ecosystems.

[0022] Module for capturing customer data in real time: This module collects live multi-channel data streams from various enterprise sources, including CRM, ERP, social media, customer support chats, web interactions, and mobile apps. It ensures continuous collection of structured and unstructured data, such as behavioral signals, feedback, transaction logs, and sentiment indicators. The data is preprocessed, tagged, and normalized to enable seamless integration with downstream analytics and decision-making components.

[0023] Contextual analytics and sentiment detection module: Using natural language processing (NLP), emotion recognition, and contextual analytics, this module interprets real-time data to detect customer emotions, intent, and experience gaps. It identifies triggers for dissatisfaction, cues of urgency, and engagement patterns. This helps to more accurately understand customer needs and tailor responses accordingly, forming the analytical backbone for proactive CX interventions.

[0024] Autonomous agent-based decision-making machine: At the heart of the system is an agent-based AI engine powered by reinforcement learning and goal-oriented decision-making. Each digital agent operates with a defined intent to improve specific CX KPIs (e.g., satisfaction, loyalty, conversion). The engine continuously learns from past interactions, makes autonomous decisions in real time, and triggers appropriate actions, such as offering discounts, escalating cases, or changing service processes, without manual intervention.

[0025] Customer Journey Mapping and Prediction Module: This module uses time series models and predictive analytics to create dynamic journey plans for each customer. It forecasts future steps, churn risk, and conversion potential by analyzing historical behavior and current interaction patterns. The system uses these insights to proactively guide customers toward positive outcomes and personalize experiences along the evolving journey.

[0026] Module for orchestrating personalized experiences: This layer executes the actions recommended by the decision engine by orchestrating personalized content, recommendations, or services across customer-facing channels. It integrates with APIs from web platforms, email systems, mobile apps, and contact centers to enable consistent and tailored interactions and ensure that responses are timely, relevant, and contextual.

[0027] Feedback loop and continuous learning module: The feedback loop module captures the impact of all interventions and continuously measures customer responses, system performance, and business results. This feedback is fed into the AI ​​agents' learning mechanisms to refine future decisions. This allows the system to self-correct, adapt to new conditions, and evolve over time to improve CX effectiveness.

[0028] Security, Compliance and Ethical Governance Module: To ensure responsible AI use, this module enforces data protection, regulatory compliance (e.g., GDPR, HIPAA), and ethical boundaries in agent behavior. It maintains audit trails, data anonymization protocols, and consent management features to create trust and transparency in all customer interactions.

[0029] The invention is explained again below with reference to the figure. It shows: Fig. : a proactive agent-based AI system (100) to optimize the customer experience in real time in enterprise platforms.

[0030] Fig.illustrates a proactive agent-based AI system (100) for real-time customer experience optimization in enterprise platforms. The proactive agent-based AI system (100) for real-time customer experience optimization in enterprise platforms works by continuously capturing and analyzing customer interactions across all enterprise channels using the Real-Time Customer Data Capturing Module, which streams live data from CRM, ERP, web, mobile, and support systems. This data is passed to the Context-Aware Analytics and Sentiment Detection Module, where advanced NLP and sentiment analysis identify emotional cues, intent, and experience gaps in real time. The insights gained are processed by the Autonomous Agentic Decision-Making Engine, where AI agents independently decide and initiate actions, such as:Offering incentives, triggering alerts, or escalating issues based on previously trained CX optimization goals. At the same time, the Customer Journey Mapping and Prediction Module creates dynamic, real-time behavior profiles and predicts customer actions to enable proactive interaction strategies. The selected actions are then executed via the Personalized Experience Orchestration Layer, which communicates tailored content, messages, or services through the appropriate customer-facing channels. After the interaction, the Feedback Loops and Continuous Learning Module evaluates customer responses, measures the results, and feeds the learning signals back into the agent-based engine for adaptive improvement.Throughout the process, the Security, Compliance, and Ethical Governance module ensures that all operations adhere to data protection regulations and ethical AI standards, ensuring trust, security, and regulatory compliance at every stage of the customer experience lifecycle.

Claims

[1] A proactive, agent-based AI system (100) for optimizing the customer experience in enterprise platforms in real time, comprising: (a) a real-time customer data input module configured to continuously collect, normalise and label structured and unstructured data from multiple enterprise sources, including CRM, ERP, web interfaces, mobile applications and customer service platforms; (b) a context-aware analytics and sentiment detection module adapted to perform real-time analysis of incoming data streams using natural language processing, emotion detection and contextual intent classification to identify customer experience patterns and sentiment indicators; c) an Autonomous Agentic Decision-Making Engine comprising a reinforcement learning-based multi-agent framework configured to evaluate detected experience contexts and autonomously initiate optimal intervention strategies based on predefined customer experience key performance indicators (KPIs); d) a Customer Journey Mapping and Prediction Module configured to create dynamic behavioral profiles, generate real-time journey maps, and predict future customer actions or churn risks based on historical and current data; (e) a Personalized Experience Orchestration Module connected to multiple customer-facing channels and configured to deliver contextually relevant, personalized content, offers or services based on agent decisions; (f) a feedback loop and continuous learning module designed to collect outcome data from implemented interventions and continuously update the learning parameters of the agent-based machine to improve future decisions; and g) a security, compliance and ethical governance module configured to enforce data protection, audit logging, consent management and regulatory compliance in enterprises, with all modules interconnected to enable autonomous, adaptive and real-time optimization of the customer experience across the entire enterprise platform. [2] The system (100) of claim 1, wherein the real-time customer data ingestion module supports ingestion of streaming data via APIs, message queues, and data lakes in formats such as JSON, XML, and CSV. [3] The system (100) of claim 1, wherein the contextual analytics and sentiment detection module uses deep learning-based NLP models to detect triggers for customer dissatisfaction within 500 milliseconds of the interaction. [4] The system (100) of claim 1, wherein the Autonomous Agentic Decision-Making Engine comprises domain-specific digital agents that cooperate or compete with each other to maximize specific CX performance goals. [5] The system (100) of claim 1, wherein the customer journey mapping and prediction module uses LSTM or transformer-based sequence models to predict the next best action or the risk of customer churn. [6] The system (100) of claim 1, wherein the Personalized Experience Orchestration Layer is integrated into enterprise communication platforms including email systems, web personalization engines, and chatbot frameworks. [7] The system (100) of claim 1, wherein the feedback loop and continuous learning module measures metrics such as response rate, resolution time, and sentiment delta to optimize the agent-related policies. [8] The system (100) of claim 1, wherein the security, compliance, and ethical governance module implements automatic PII masking, access control, and audit trails for each CX transaction. [9] The system (100) of claim 1, wherein all modules are deployed in a cloud-native microservices architecture to enable scalability, modularity, and continuous deployment within enterprise IT ecosystems.

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