DESC:
FIELD OF INVENTION
[0001] The present invention relates to a conversational AI, and in particular relates to a conversational AI platform, BharatGPT, with contextual and grounded Generative AI.

BACKGROUND OF THE INVENTION
[0002] Large Language Models (LLMs) are machine learning models that utilize deep learning algorithms to process and understand natural language. By training on extensive text data, LLMs gain knowledge of language formats, patterns, and entity relationships. These models serve various language-related tasks, such as language translation, sentiment analysis, chatbot conversations, and more.
[0003] However, existing LLMs have been primarily built on static information, lacking support for real-time transactions and updates. This limitation hampers their ability to create Enterprise Virtual Assistants like chatbots, voicebots, and videobots. Consequently, the challenge persists in efficiently handling large volumes of data, leading to extensive processing and reduced system speed. These factors contribute to the current inefficiency of the system, which remains unaddressed. However, conventionally LLM’s models fail to provide such AI platform that requires less memory and computing and couldn’t overcome burden on GPU. Therefore, there is an unmet need to provide a system that can utilizes lesser memory, computing power and ensures faster processing GPU and makes the whole system more resource efficient.
[0004] Furthermore, there is currently no system based on a conversational AI platform using contextual and grounded generative AI, can generate output responses with updated real time transactions. More specifically, grounded artificial intelligence establishes a connection between an AI system and its real-world environment. Its objective is to develop AI systems that are rooted in real-time data, experiences, and interactions with the world. As a result, these systems possess decision-making abilities and generate highly accurate and contextually aligned responses in line with the current environment. Grounded AI goes beyond static information and adapts to dynamic situations, allowing for the provision of contextually relevant and meaningful outputs. Contextual AI interprets data within a specific context. With the assistance of contextual AI, a system gains the ability to comprehend information more effectively and considers relevant contextual factors to provide appropriate responses to user inputs. However, until now, only models that relied on static information were available, lacking the inclusion of real-time transactions as a feature of the system or model. The present invention overcomes all the hurdles that previously existed and provides an efficient system.
[0005] Thus, there is an unmet need for a large language model or system that demands less computational power and memory, while enabling real-time transactions. Moreover, the present invention provides a solution to alleviate the strain on GPUs, resulting in a more resource-efficient and effective system.

OBJECT OF THE INVENTION
[0006] Keeping the above in mind objective of the present invention is to provide such a conversational AI platform with contextual and grounded Generative AI, titled BharatGPT, which is omni-channel, multi-lingual, and multi-format tool that has been fine-tuned to the needs of users.
[0007] Another objective of the present invention is to design a system that can either for handle static information or support the real-time transactions and updates.

SUMMARY OF THE INVENTION
[0008] The present invention relates to artificial intelligence, and in particular, relates to conversational artificial intelligence integrated with generative artificial intelligence.
[0009] The AI system of the present invention needs relatively lesser computing power, and memory as the instant invention adopts a multi-layered approach of NLP such as Natural Language Understanding (NLU) & Natural Language Generation (NLG) addressing user queries, with separate layers responsible for different tasks such as deep learning with generative AI (unsupervised); supervised learning; AIML (Artificial Intelligence Markup Language); and the context-based auto-suggestion (in the reverse order). This implies that the burden on the general processing unit (GPUs) can be reduced and the whole system can be more resource-efficient.
[0010] In an embodiment, the present invention discloses a method for optimizing artificial intelligence algorithms for enterprise-grade applications, the method comprising: receiving user-specific queries, including preferences, behaviors, and linguistic nuances, from one or more enterprise systems, processing using user queries by applying (Classic Natural Language Processing (NLP), Retrieval-Augmented Generation (RAG), fine-tuned Large Language Models (LLM ) for deep multitasking learning, adjusting a pre-trained artificial intelligence models using supervised and reinforcement learning techniques based on the received user-specific data to generate domain-specific conversational outputs, integrating a custom knowledge base with the adjusted models, wherein the knowledge base is dynamically updated through user modifications and enterprise-specific data feeds, establishing real-time communication with Enterprise Resource Planning (ERP) systems and Customer Relationship Management (CRM) systems via standardized Application Programming Interfaces (APIs), conducting real-time financial transactions using an payment gateway compliant, enabling natural and contextually relevant multi-turn conversations between users and the artificial intelligence system through context-aware response generation mechanisms and generating and delivering dynamic AI-powered multimedia outputs, including generative videos and interactive digital twins, to users based on contextual inputs.
[0011] In another embodiment, said method further includes user feedback analysis, including interaction ratings and error logs, to dynamically fine-tune the pre-trained artificial intelligence models and improve response accuracy over time.
[0012] In yet another embodiment, the supervised and reinforcement learning techniques include multi-language training pipelines configured to optimize the artificial intelligence models for multilingual support across 12 or more languages.
[0013] In another embodiment, the custom knowledge base includes both structured data repositories, such as SQL databases, and unstructured data sources, including documents and multimedia files, processed using embedding models for vector-based retrieval.
[0014] In yet another embodiment, the standardized Application Programming Interfaces (APIs) include security measures such as token-based authentication, endpoint validation, and rate limiting to prevent unauthorized access and ensure compliance with enterprise security policies.
[0015] In a further embodiment, said method monitors real-time financial transactions for anomalies using machine learning-based fraud detection algorithms integrated with the payment gateway.
[0016] In an embodiment, the context-aware response generation mechanisms include a dynamic context engine that tracks session data, geolocation, and temporal information to maintain conversational coherence across multiple interactions.
[0017] In another embodiment, said method further includes employing generative AI models to create domain-specific training datasets using data augmentation techniques for fine-tuning the artificial intelligence models.
[0018] In yet another embodiment, the generative videos and interactive digital twins are created using a combination of retrieval-augmented generation (RAG) techniques and domain-specific templates, ensuring consistency and relevance to user requirements.
[0019] In an embodiment, said method also includes compliance mechanisms to dynamically adapt the artificial intelligence system to evolving regulatory frameworks, including GDPR, HIPAA, and CCPA, by updating data processing guardrails and system configurations.
[0020] In another embodiment, said method also includes restricting system access using role-based access control (RBAC) and encryption-based authentication mechanisms, ensuring availability exclusively to authorized organizations.
[0021] In another aspect, the present invention discloses a conversational AI platform for multi-domain applications executed by a server, the platform comprising: an input pre-processing unit configured to validate, sanitize, and pre-process user inputs, a dynamic knowledge management system configured to enable users to add, modify, and retrieve domain-specific knowledge, an API connectivity interface configured to integrate the platform with enterprise systems, the API connectivity interface comprising predefined protocols and schemas for facilitating data exchange and workflow automation with Enterprise Resource Planning (ERP) systems and Customer Relationship Management (CRM) systems, a secure transaction processor comprising an integrated payment gateway and configured to process financial transactions in real-time, a context engine comprising metadata enrichment algorithms and configured to maintain conversational context across multi-turn interactions, a generative AI output generator configured to create dynamic outputs based on user inputs and domain-specific insights, wherein the generative AI output generator comprises a generative model fine-tuned on domain-specific datasets for generating context-aware outputs; and a multimedia rendering module configured to produce interactive videos and digital twins and a security and compliance system configured to enforce enterprise-grade security and regulatory compliance.
[0022] In one embodiment, the input pre-processing unit further comprises a natural language processing (NLP) module configured to perform language detection, tokenization, and sentiment analysis of user inputs.
[0023] In a further aspect, the dynamic knowledge management system comprises a machine learning-based indexing engine configured to automatically classify and tag domain-specific knowledge for faster retrieval.
[0024] In another aspect, the API connectivity interface supports integration with third-party analytics tools, including data visualization platforms and machine learning model monitoring systems, via extensible APIs.
[0025] In an aspect, the secure transaction processor further comprises a fraud detection subsystem configured to monitor and analyze transaction patterns using anomaly detection algorithms to identify potential fraudulent activities.
[0026] In another aspect, the context engine is further configured to utilize session-based storage for retaining user interaction histories and enabling seamless continuation of conversations across multiple sessions.
[0027] In yet another aspect, the generative AI output generator further comprises a retrieval-augmented generation (RAG) pipeline configured to retrieve contextually relevant data from the dynamic knowledge management system before generating outputs.
[0028] In a further aspect, the multimedia rendering module is further configured to adapt interactive video outputs dynamically to different user devices, including desktops, mobile devices, and augmented reality (AR) platforms.
[0029] In yet another aspect, the security and compliance system further comprises a real-time auditing engine configured to generate compliance reports based on user activity logs and system interactions, ensuring adherence to regulatory standards.
[0030] In an aspect, the security and compliance system includes a data anonymization module configured to mask sensitive user data during processing to enhance privacy protection and ensure compliance with GDPR and HIPAA regulations.
[0031] Various objects, features, aspects, and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF DRAWINGS
[0032] In the figures, similar components and/or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label with a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.
[0033] FIG. 1 illustrates a system based on generative artificial intelligence (GenAI) integrated with conversational artificial intelligence with various functional and generative capabilities for interactive messaging and along with real-time transactions, in accordance with the present invention.
[0034] FIG. 2 is a flowchart illustrating a method for providing interactive messaging along with real-time transactions, in accordance with the present invention.
[0035] FIG. 3 illustrates an architecture of the GenAI tool BharatGPT, in accordance with the present invention.
[0036] FIG. 4. illustrates the Platform Architecture Diagram employs a sophisticated three-layered approach to ensure enhanced answerability and reliability and highlights the overarching flow of information across multiple components and layers (NLP, RAG, LLM),
[0037] FIG. 5 illustrates specific mechanisms and internal workflows that operationalize the platform’s functionality.
[0038] FIG.6 illustrates the BharatGPT Model Training Pipeline.

DETAILED DESCRIPTION OF THE INVENTION
[0039] As required, detailed embodiments of the present invention are disclosed herein, however, it is to be understood that the disclosed embodiments are merely exemplary of the invention which may be embodied in various forms. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present invention in virtually any appropriately detailed structure. It is to be understood that other embodiments may be utilized, and structural changes may be made without departing from the scope of the invention.
[0040] All referenced methods are incorporated herein by reference in their entirety. Furthermore, where a definition or use of a term in a reference, which is incorporated by reference herein, is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.
[0041] While certain aspects of conventional technologies have been discussed to facilitate disclosure of the invention, Applicants in no way disclaim these technical aspects, and it is contemplated that the claimed invention may encompass one or more of the conventional technical aspects discussed herein.
[0042] The present invention may address one or more of the problems and deficiencies of the prior art discussed above. However, it is contemplated that the invention may prove useful in addressing other problems and deficiencies in a number of technical areas. Therefore, the claimed invention should not necessarily be construed as limited to addressing any of the particular problems or deficiencies discussed herein.
[0043] In this specification, where a document, act or item of knowledge is referred to or discussed, this reference or discussion is not an admission that the document, act or item of knowledge or any combination thereof was at the priority date, publicly available, known to the public, part of common general knowledge, or otherwise constitutes prior art under the applicable statutory provisions; or is known to be relevant to an attempt to solve any problem with which this specification is concerned.
[0044] As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” include plural referents unless the content clearly dictates otherwise. As used in this specification and the appended claims, the term “or” is generally employed in its sense including “and/or” unless the context clearly dictates otherwise.
[0045] The present invention aims to provide a system (100) with high speed and having greater efficiency along with using lesser memory and requires lesser computing power.
[0046] FIG.1 and FIG3. illustrate a system (100) based on generative artificial intelligence integrated with conversational artificial intelligence with various functional and generative capabilities for interactive messaging and along with real time transactions and the architecture, in accordance with the present disclosure.
[0047] The system (100) comprises application programming interfaces (112), network connection (118) configured to connect to an user device and service providers for real time transaction data updates or query updates of user, and to avail available data on internet source; and I/O interfaces (114), wherein user feeds an input data using a user computing device such as a smartphone, laptop, and computer; and a Central Processing Unit (104a) or GPU(104b) processes the input data by applying different algorithms like NLP, NLU & NLG, machine learning etc., and, generates an output response.
[0048] The system (100) of the present invention comprises a conversational AI module that employs NLG (natural language generation) and NLU (natural language understanding) which are subsets of NLP (natural language processing). These algorithms help in understanding user input data, finding syntactic and semantic analysis, and detecting errors. They also identify the sense of words and interpret input data in the context of a sentence, whether it's in the form of text, video, or voice. If errors are detected after analyzing the input data, the system rectifies them and generates structured data as the output response. It is evident that these algorithms not only find flaws and mistakes but also understand the meaning of the input data and possess decision-making abilities to generate meaningful and understandable output responses for the user.
[0049] In an embodiment of the present disclosure, I/O interfaces (114) may be provided by a server (102) of a service provider to allow the user to initiate data input from the user end. Further, the system of the present disclosure comprises a memory (106) that stores user input queries or responses, wherein, the memory (106) is a repository for storing and managing collections of data in the form of files, documents, etc.
[0050] Generative AI utilizes neural networks to identify various patterns and structures within existing data, enabling the generation of new and original information. Generative AI leverages diverse learning techniques to produce high-quality outputs. For example, when dealing with poor-quality speech that is challenging to comprehend, the GenAI assists in delivering output of exceptional quality.
[0051] The conversational AI module (108) for dialogue management, data sources by leveraging open data input from the internet such as text, voice, and video capabilities, in order to create virtual assistants. The generative AI module (110) of the present invention has many additional features and is thereby considered as EnterpriseGPT. More particularly the generative AI module (110) of the present invention implements a responsible AI with generative capabilities, with better governance and safety net, along with informational and end-to-end transactional capabilities, as per the requirement and business needs of the various organizations.
[0052] The present disclosure of invention discloses a conversational AI module (108) having a number of advantages such as having the option to add a custom knowledge base. Further, the module can be integrated with any Enterprise Resource Planning (ERP)/Customer Relationship Management (CRM) systems, and application programming interfaces (APIs) (112) for real-time transactions. Generative artificial intelligence of the present invention also has the capability of integrating payment gateway and social security number-based authentication for KYC, like Aadhar number. Additionally, Generative artificial intelligence of the present invention has many features, viz., documents-to-text (fine-tuned AI-based OCR, hand-written documents are also supported), text-to-Q&A (Q&A Generator), text-to-voice (voice cloning), text-to-video (video cloning), sentiment analysis and many more.
[0053] The generative AI module (110) integrated with the conversational AI module (108), needs relatively lesser computing power and memory as the present invention discloses adopting a multi-layered approach of NLP (NLU & NLG) addressing user inputs/queries, with separate layers responsible for different tasks such as deep Learning with generative AI (unsupervised); supervised learning; AIML (Artificial Intelligence Markup Language); and the context-based auto-suggestion (in the reverse order). Deep neural networks are designed to mimic the structure and function of the human brain by employing multiple layers of interconnected neurons and algorithms. This design allows them to excel in capturing complex patterns within the input data. By considering these factors and employing various layers of processing, deep neural networks provide improved and enhanced quality in the generated output response.
[0054] The system (100) of the present invention, due to different layering approaches reduces the burden on the GPUs and the whole system (100) can be more resource efficient, as the present invention doesn’t always generate answers with Gen AI for the frequent intents, as they may be answered from other layers. Further, the system (100) employs efficient word embeddings i.e., mapping of a discrete — categorical — variable to a vector of continuous numbers. Furthermore, in the context of neural networks, embeddings are low-dimensional and are learned continuous vector representations of discrete variables. This makes machine learning faster in the case of generative AI requires less memory and is less compute intensive.
[0055] Referring to Fig. 1, in the preferred embodiment of the present invention, the system is leveraging the available/open data from the internet, and additional information, with an option to fine-tune and add more content specific with respect to the context of the region, sector/domain, client/business, and user case(s), which helps deliver updated and relevant information to the users’ input queries.
[0056] The present disclosure discloses the system (100) may handle more than 12 Indian languages and over 120 foreign languages, with text, voices, and videos. The system (100) of the present invention is the world’s first and the highest return on investment (ROI) -delivering a human-centric conversational AI platform, with Generative AI capabilities. It provides all the required features needed to build and manage Chatbots across communication channels like dialogue/conversation management tools. A conversational AI platform with contextual and grounded Generative AI is used for hundreds of organizations, which include IRCTC, LIC, IGL, KSRTC, Indian Navy (GRSE), Max Life Insurance, NPCI, BHIM-UPI, Mahindra, Government of India, and many more.
[0057] Referring to Fig. 2, in a preferred embodiment of the present invention, illustrates a flowchart of a method for providing interactive messaging along with real-time transactions. Fig.2 representing the flowchart (200) of the present disclosure discloses the method steps which are performed as follows:
Step 202: Receive user-specific queries, including preferences, behaviors, and linguistic nuances, from one or more enterprise systems
Step 204: Process using user queries by applying (Classic Natural Language Processing (NLP), Retrieval-Augmented Generation (RAG), and fine-tuned Large Language Models (LLM) for deep multitasking learning.
Step 206: Adjust a pre-trained artificial intelligence models using supervised and reinforcement learning techniques based on the received user-specific data to generate domain-specific conversational outputs.
Step 208: Integrate a custom knowledge base with the adjusted models, wherein the knowledge base is dynamically updated through user modifications and enterprise-specific data feeds.
Step 210: Establish real-time communication with Enterprise Resource Planning (ERP) systems and Customer Relationship Management (CRM) systems via standardized Application Programming Interfaces (APIs).
Step 212: Conduct real-time financial transactions using a payment gateway compliant.
Step 214: Enable natural and contextually relevant multi-turn conversations between users and the artificial intelligence system through context-aware response generation mechanisms.
Step 216: Generate and deliver dynamic AI-powered multimedia outputs, including generative videos and interactive digital twins, to users based on contextual inputs.
These steps are performed sequentially. Moreover, user inputs and outputs are configured to connect with I/O interface (114) to provide updated responses to the user.

[0058] Referring to Fig.3, the present invention discloses the conversational AI platform architecture employs a sophisticated three-layered approach to ensure enhanced answerability and reliability. This architecture is distinctly designed to maximize response accuracy and context relevance using a multi-tiered fallback mechanism. The design integrates classic NLP technologies, Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs) in a fallback flow. The first layer addresses straightforward queries using deterministic NLP models, ensuring fast and precise responses. The first layer leverages tried-and-tested classic Natural Language Processing (NLP) techniques. These include several advanced components such as Pre-Processing including Tokenization, Stopword Removal, Stemming, Lemmatization, and Noise Removal. Additionally, it features intent recognition to identify the purpose behind the user's input, entity recognition to extract entities from user queries and dependency parsing for understanding relationships between entities. This layer acts as the foundational processing unit for straightforward queries and ensures high-speed, deterministic responses for known patterns. The second layer employs RAG to combine factual knowledge bases with generative AI, providing contextual depth and relevance. The second layer introduces RAG-based processing, combining generative AI capabilities with a context-rich knowledge base. The second layer includes document retrieval in which relevant data is pulled from the vector store in response to user inputs. It further includes contextual fusion in which retrieved documents are used with prompt to LLM with grounded data to refine responses. The final layer utilizes LLMs as a fallback mechanism, ensuring coherent and meaningful responses even in complex or edge-case scenarios. In cases, where the previous layers yield insufficient results, the architecture includes a third layer Fallback Flow to Generative LLMs. This layer provides flexibility to incorporate third-party LLMs, open-source LLMs, and domain-specific LLMs, ensuring that the platform can dynamically adapt and deliver robust responses even in complex scenarios. This ensures graceful handling of edge cases, providing conversational coherence even for unstructured or unforeseen queries. This layer further includes a proprietary scoring mechanism dynamically prioritizes fallback execution to minimize latency while maintaining response quality. At the core of this architecture, it includes proprietary Intelligence Layer which dynamically evaluates and optimizes responses through advanced context retention, scoring mechanisms, and reinforcement learning. The proprietary intelligence layer includes dynamic scoring mechanism which evaluates responses from all layers and selects the optimal one for delivery. It further includes reinforcement learning for feedback loops which continuously improves system performance by integrating user feedback into conversational AI platform fine tuning. Proprietary algorithms ensure conversational context is retained across multi-turn interactions, enabling highly personalized user experiences. This modular framework incorporates diverse, extensible components customized for varied use cases such as knowledge bases, third-party systems integration and context engine. These knowledge bases include both structured and unstructured data repositories, enriched with domain-specific insights. These third-party systems integration includes seamless API connectivity with external services such as CRMs, ticketing systems, and payment gateways. These context engine includes dynamically enriches user queries with metadata, geolocation, and session data to optimize downstream processing. The context engine operates as a as a middleware layer, bridging user interactions and the backend. The key features include real-time query augmentation, Multi-Dimensional Context Handling and session persistence. The real-time query augmentation injects contextual metadata to disambiguate user intent. This Multi-Dimensional Context Handling supports temporal, spatial, and behavioral dimensions for comprehensive user modeling. The session persistence tracks user sessions across channels to maintain continuity. Complementing this is a sophisticated Context Engine, which enables seamless query augmentation and session continuity, ensuring an intuitive and consistent user experience. Real-time data analytics, integrated with anomaly detection and performance monitoring dashboards, further enhance operational efficiency and decision-making capabilities. Furthermore, the conversational AI platform modular framework supports integration with domain-specific knowledge bases and third-party systems, while its robust security measures, including multi-layered encryption, data masking, and compliance with global privacy standards, safeguard user data and ensure end-to-end security. This innovative architecture sets a new standard for conversational AI systems, offering unparalleled scalability, contextual intelligence, and reliability for diverse use cases.
[0059] The enhancement pipeline involves several critical steps to ensure the continuous improvement and accuracy of the model. Feedback analysis is the initial step, gathering user feedback to improve model responses and relevance. This is followed by retraining and fine-tuning, where the model is updated based on the feedback to enhance its accuracy. Error detection and diagnosis are crucial for identifying and rectifying errors in the model's predictions or outputs. Model evaluation and testing then validate the model's performance through rigorous testing. Once validated, model deployment rolls out the updated model in production environments. Performance monitoring continuously tracks the model's operational efficiency and accuracy, while continuous learning implements iterative learning mechanisms to ensure the model evolves with changing user requirements.
[0060] Further, a robust analytics pipeline provides real-time insights into system performance and user behaviour. It includes a data warehouse that consolidates query logs, system decisions, and user interactions for analytics and reporting. The visualization dashboard displays key metrics, including query resolution rates, fallback layer usage, and latency statistics. The anomaly detection uses machine learning-based mechanisms to flag potential issues in system behaviour or user activity. In security architecture, Security is embedded at every layer of this architecture to ensure data integrity and compliance with global standards. End-to-End Encryption is employed, meaning all communications are encrypted using TLS, ensuring secure transmission of user data. Data masking is employed, and sensitive information is anonymized before processing, protecting user privacy under PII security. Role-Based Access Control (RBAC) ensures granular access to system components, minimizing exposure to threats. The secure data storage employs PostgreSQL databases with multi-layered encryption for storing sensitive user and system data. Compliance-Ready aligns with all major country-specific data protection norms and regulations.
[0061] Incremental & Adaptive Learning (from Generative Q&As to Classic NLP Trained Data) represents a hybrid learning approach where the system evolves by combining generative AI capabilities with classic NLP-trained data. Incremental Learning allows the system to collect new data points from user interactions in the form of Q&As, gradually updating the trained datasets without requiring complete retraining. Adaptive Learning enables the model to adapt dynamically to changing user behavior or new data trends, bridging the flexibility of generative AI with the accuracy and reliability of classic NLP models. For example, if a query starts as a generative response but repeats with a similar context, it may be flagged for template optimization using classic NLP. Over time, the system intelligently decides whether to use a generative approach or fall back to NLP-trained data for specific queries based on efficiency, response time, or user satisfaction. This seamless transition ensures the model evolves and remains effective in various scenarios.
[0062] Referring to Fig.4, in a preferred embodiment of the present invention, covers Platform Architecture. While the platform architecture highlights the overarching flow of information across multiple components and layers (NLP, RAG, LLM), this system architecture delves into the specific mechanisms and internal workflows that operationalize the platform’s functionality. The system architecture is designed to integrate fine-tuned models, and enterprise-level components to deliver robust and secure conversational AI services. It incorporates modular pipelines for input preprocessing, context-aware response generation, and stringent compliance with security protocols, ensuring both scalability and reliability. The process begins with Fine-Tuned ASR (Automatic Speech Recognition) for voice-based inputs and Input Guardrails, which enforce validation, sanitization, and pre-processing for incoming data. This ensures that the input is in an optimal format for downstream processing and prevents injection attacks or malformed queries. The Chat Agent acts as the central routing and orchestration layer. It connects to Fine-Tuners for adaptive learning, ensuring context-specific responses. The Response Generators, produce primary outputs based on the model. The Output Guardrails, which enforce business logic and compliance, validate responses before delivery to users. The Router dynamically selects the most appropriate workflow based on the query type, whether to utilize: Multi-Modal for generative outputs, Enterprise RAG (Retrieval-Augmented Generation) pipelines for responses requiring factual grounding, and action agents for task-based operations involving Integrated Systems. The RAG Pipeline enhances factual accuracy by retrieving information from Enterprise Data Sources for domain-specific content. Integrated Systems, such as third-party APIs and databases, to provide operational responses. PII Guardrails (Personally Identifiable Information) is embedded within the pipeline to ensure compliance with data privacy regulations, preventing sensitive information leakage. Further, conversational AI platform at the core of the system, operates in a multi-modal architecture, integrating both text and voice modalities. Fine-tuners enable domain-specific optimization, ensuring the model adapts to the enterprise's needs without retraining the entire model. The specialized action agents handle task execution using action/tool calling, including external system triggers and transaction handling. They bridge the conversational AI platform with enterprise-level workflows, ensuring end-to-end integration and automation. The security and compliance layers have guardrails for both input and output processing, combined with the PII Guardrails, to ensure multi-layered security. These layers enforce compliance with GDPR, HIPAA, and other global regulations while securing data flow between modules.
[0063] Referring to Fig.5, in a preferred embodiment of the present invention, covers the architecture and workflow for training the model and its deployment with Retrieval-Augmented Generation (RAG) capabilities. It is divided into two main sections: Training/Fine-Tuning and RAG Deployment. The Training and Fine-Tuning Process involves preparing data pipelines, including proprietary and open datasets such as multilingual corporation and client-specific knowledge bases, to ensure the model is effectively trained and fine-tuned for optimal performance. The model and tokenizer are loaded using predefined training arguments, and configurations are set for distributed training. A pre-trained base model and tokenizer are imported using frameworks like AutoModelForCausalLM and AutoTokenizer. In data loading, the training datatsets (12+ languages) are preprocessed with truncation, padding, and formatting to optimize training quality. In distributed training, a training loop processes the dataset through tokenization, truncation, and padding. The model undergoes multiple evaluation cycles, and metrics such as training loss are logged for analysis. In the evaluation and fine-tuning model, Post-training, the model undergoes fine-tuning to ensure performance alignment with desired objectives. Tools like TensorBoard are used to log and monitor the training process.
[0064] The Retrieval-Augmented Generation (RAG) process involves retrievers, where virtual assistants or prompts interact with the system to process user queries. This approach allows the platform to integrate retrieved information seamlessly into the generative process, enhancing the relevance and accuracy of responses. : A client-specific knowledge base (e.g., ERP systems, CRM, FAQs) provides contextual information. Relevant chunks of data are retrieved. The system uses embedding models to convert both queries and documents into vector representations for effective similarity matching. The retrieved embeddings are stored in a vector database, enabling efficient query matching. The processed query, enriched with relevant context, is sent to the generator for a final response. The fine-tuned conversational AI model generates accurate and contextually relevant responses, ensuring high reliability in deployment scenarios. This system pipeline includes scalable model training that handles large datasets across multiple languages and domains. Context-aware responses are achieved by integrating contextual data from external knowledge bases through RAG. Continuous improvement is ensured with feedback mechanisms that refine training and response quality over time. Additionally, the architecture is modular, providing customizability that makes it adaptable for different industries and applications.
[0065] Implementation use cases in real-world applications include healthcare assistance, where the system functions as an intelligent conversational assistant for healthcare providers. It enables patients to access information on symptoms, medications, treatment options, and more. Additionally, it facilitates appointment scheduling, prescription clarification, and timely reminders for medication and vaccinations. The technical implementation involves user inputs, whether text or voice, are processed through Fine-Tuned ASR (for speech inputs) or Input Guardrails to ensure accuracy and compliance. These inputs are routed to the RAG pipeline, leveraging fine-tuned LLM trained on healthcare datasets and enterprise-specific medical repositories. The Context Engine enhances the interaction by ensuring relevant and personalized responses.
[0066] Another user case is Customer Support Automation. Enterprises deploy the system as an AI-driven virtual assistant to handle customer queries across industries, including e-commerce, telecom, and banking. The solution manages inquiries related to product support, billing, troubleshooting, and more, ensuring swift and accurate resolution. The technical implementation involves Incoming queries being routed through the Chat Agent, with input validated by guardrails. Fine-tuned LLM, optimized for domain-specific knowledge, processes these inputs, while the RAG pipeline retrieves evidence-based answers from enterprise knowledge bases. Responses are generated in compliance with Output Guardrails to maintain security and consistency.
[0067] Another use case is government and citizen Engagement. Governments utilize the platform to provide 24/7 multilingual support for public services such as passport applications, tax filing, and grievance redressal. The technical implementation involves Queries are routed through the system’s Input Guardrails to ensure authenticity and routed to fine-tuned LLM for interpretation. The Context Engine retrieves relevant information from integrated government systems and databases. Real-time updates are facilitated via APIs, ensuring accurate and up-to-date responses.
[0068] Another use case is EdTech Personalization: Educational platforms use conversational AI platforms to provide personalized learning experiences, including interactive Q&A sessions, tutoring, and content recommendations. The technical implementation includes User inputs processed through Fine-Tuned ASR (for voice interactions) or Input Guardrails for text inputs. Fine-tuned LLM trained on educational datasets generate customized responses, leveraging RAG to provide source-backed content. The feedback loop ensures ongoing improvement in recommendations based on user engagement patterns.
[0069] In exemplary embodiment, the datasets include proprietary enterprise knowledge bases: Integrated through APIs for dynamic retrieval during RAG processing. In multilingual corpora, it ensures support for diverse languages and dialects. The training models include Pre-trained LLM fine-tuned using supervised and reinforcement learning techniques. The RAG training on real-world queries and responses to optimize retrieval accuracy and context relevance.
[0070] In exemplary embodiment, the flow diagram is as follows: a.User Input (Text/Voice) ? Input Guardrails ? Fine-Tuned ASR ? RAG Pipeline ? Fine-Tuned LLM ? Output Guardrails ? Response Delivery.
[0071] In an embodiment, the architecture incorporates a multi-tiered feedback mechanism to ensure continuous improvement. In User Feedback Capture, it includes Interaction ratings and feedback signals that allow users to highlight response accuracy and relevance. Followed by, Error logging includes logs of incorrect or suboptimal responses that are analyzed by fine-tuners for retraining the models. In Dynamic Fine-Tuning, enterprise data and user-specific interactions continuously update the model LLM and RAG pipeline for enhanced precision. The performance monitoring includes real-time dashboards that provide insights into system performance, latency, and accuracy, allowing for proactive adjustments. In compliance updates, guardrails and response generators are updated based on evolving regulations and enterprise requirements to maintain security and compliance. A conversational AI platform feedback mechanism ensures that the system evolves in line with user needs, emerging trends, and operational requirements, maintaining a state-of-the-art conversational AI solution.
[0072] According to the present invention, therefore, the system is based on generative artificial intelligence integrated with conversational artificial intelligence with various functional and generative capabilities for interactive messaging and along with real-time transactions which basically provides a system that works efficiently, solves the problem related to deal with either cumbersome data or burden on general processing unit.
[0073] The advantages set forth above, and those made apparent from the foregoing description, are efficiently attained. Since certain changes may be made in the above construction without departing from the scope of the invention, it is intended that all matters contained in the foregoing description or shown in the accompanying drawings shall be interpreted as illustrative and not in a limiting sense.
It is also to be understood that the following claims are intended to cover all of the generic and specific features of the invention herein described, and all statements of the scope of the invention that, as a matter of language, might be said to fall therebetween.

,CLAIMS:WE CLAIM
1. A method for optimizing artificial intelligence algorithms for enterprise-grade applications, the method comprising:
a. receiving user-specific queries, including preferences, behaviors, and linguistic nuances, from one or more enterprise systems;
b. processing using user queries by applying (Classic Natural Language Processing (NLP), Retrieval-Augmented Generation (RAG), fine-tuned Large Language Models (LLM ) for deep multitasking learning;
c. adjusting a pre-trained artificial intelligence models using supervised and reinforcement learning techniques based on the received user-specific data to generate domain-specific conversational outputs;
d. integrating a custom knowledge base with the adjusted models, wherein the knowledge base is dynamically updated through user modifications and enterprise-specific data feeds;
e. establishing real-time communication with Enterprise Resource Planning (ERP) systems and Customer Relationship Management (CRM) systems via standardized Application Programming Interfaces (APIs);
f. conducting real-time financial transactions using a payment gateway compliant;
g. enabling natural and contextually relevant multi-turn conversations between users and the artificial intelligence system through context-aware response generation mechanisms;
h. generating and delivering dynamic AI-powered multimedia outputs, including generative videos and interactive digital twins, to users based on contextual inputs.

2. The method of claim 1, wherein said method further comprises analyzing user feedback, including interaction ratings and error logs, to dynamically fine-tune the pre-trained artificial intelligence models and improve response accuracy over time.
3. The method of claim 1, wherein the supervised and reinforcement learning techniques include multi-language training pipelines configured to optimize the artificial intelligence models for multilingual support across 12 or more languages.
4. The method of claim 1, wherein the custom knowledge base includes both structured data repositories, such as SQL databases, and unstructured data sources, including documents and multimedia files, processed using embedding models for vector-based retrieval.
5. The method of claim 1, wherein the standardized Application Programming Interfaces (APIs) include security measures such as token-based authentication, endpoint validation, and rate limiting to prevent unauthorized access and ensure compliance with enterprise security policies.
6. The method of claim 1, said method further comprises monitoring real-time financial transactions for anomalies using machine learning-based fraud detection algorithms integrated with the payment gateway.
7. The method of claim 1, wherein the context-aware response generation mechanisms include a dynamic context engine that tracks session data, geolocation, and temporal information to maintain conversational coherence across multiple interactions.
8. The method of claim 1, further comprising employing generative AI models to create domain-specific training datasets using data augmentation techniques for fine-tuning the artificial intelligence models.
9. The method of claim 1, wherein the generative videos and interactive digital twins are created using a combination of retrieval-augmented generation (RAG) techniques and domain-specific templates, ensuring consistency and relevance to user requirements.
10. The method of claim 1, said method further comprises implementing compliance mechanisms to dynamically adapt the artificial intelligence system to evolving regulatory frameworks, including GDPR, HIPAA, and CCPA, by updating data processing guardrails and system configurations.
11. The method of claim 1, said method further comprises restricting system access using role-based access control (RBAC) and encryption-based authentication mechanisms, ensuring availability exclusively to authorized organizations.
12. A conversational AI platform for multi-domain applications executed by a server, the platform comprising:
an input pre-processing unit configured to validate, sanitize, and pre-process user inputs;
a dynamic knowledge management system configured to enable users to add, modify, and retrieve domain-specific knowledge;
an API connectivity interface configured to integrate the platform with enterprise systems, the API connectivity interface comprising predefined protocols and schemas for facilitating data exchange and workflow automation with Enterprise Resource Planning (ERP) systems and Customer Relationship Management (CRM) systems;
a secure transaction processor comprising an integrated payment gateway and configured to process financial transactions in real-time;
a context engine comprising metadata enrichment algorithms and configured to maintain conversational context across multi-turn interactions;
a generative AI output generator configured to create dynamic outputs based on user inputs and domain-specific insights, wherein the generative AI output generator comprises a generative model fine-tuned on domain-specific datasets for generating context-aware outputs; and a multimedia rendering module configured to produce interactive videos and digital twins;
a security and compliance system configured to enforce enterprise-grade security and regulatory compliance.

13. The platform of claim 11, wherein the input pre-processing unit further comprises a natural language processing (NLP) module configured to perform language detection, tokenization, and sentiment analysis of user inputs.

14. The platform of claim 11, wherein the dynamic knowledge management system further comprises a machine learning-based indexing engine configured to automatically classify and tag domain-specific knowledge for faster retrieval.

15. The platform of claim 1, wherein the API connectivity interface supports integration with third-party analytics tools, including data visualization platforms and machine learning model monitoring systems, via extensible APIs.
16. The platform of claim 11, wherein the secure transaction processor further comprises a fraud detection subsystem configured to monitor and analyze transaction patterns using anomaly detection algorithms to identify potential fraudulent activities.
17. The platform of claim 11, wherein the context engine is further configured to utilize session-based storage for retaining user interaction histories and enabling seamless continuation of conversations across multiple sessions.
18. The platform of claim 11, wherein the generative AI output generator further comprises a retrieval-augmented generation (RAG) pipeline configured to retrieve contextually relevant data from the dynamic knowledge management system before generating outputs.
19. The platform of claim 11, wherein the multimedia rendering module is further configured to adapt interactive video outputs dynamically to different user devices, including desktops, mobile devices, and augmented reality (AR) platforms.
20. The platform of claim 11, wherein the security and compliance system further comprises a real-time auditing engine configured to generate compliance reports based on user activity logs and system interactions, ensuring adherence to regulatory standards.
21. The platform of claim 1, wherein the security and compliance system includes a data anonymization module configured to mask sensitive user data during processing to enhance privacy protection and ensure compliance with GDPR and HIPAA regulations.