A unified multimodal GenAI platform integrating GraphRAG multi-agent systems and custom language models for intelligent document processing and knowledge synthesis.

Retrieval-Augmented Generation (RAG) based enterprise systems on document reasoning tasks are getting to grow more based on Large Language Models (LLMs); however, non-vector-based RAG pipelines continue to show inadequate relational consistency, multi-document aggregation, and task-specific decision support. This work presents a unified Generative Artificial Intelligence (GenAI) platform that integrates a multi-agent system with Graph-based RAG (GraphRAG) to support complex, multi-task reasoning over structured and unstructured data. The framework combines knowledge graph construction, dense retrieval, and a custom language model to enable accurate and context-aware responses across tasks such as document question answering, entity extraction, text-to-SQL generation, and fact verification. A modular pipeline is designed for task classification, agent routing, retrieval-oriented reasoning, and task-specific execution, including ATS resume evaluation. The suggested platform consists of 5 conceptual layers and may be applied to solve multi-PDF questions and analyze resumes in an automated Applicant Tracking System (ATS). The platform has a set of six custom-trained LLMs, the largest of which is a 175-billion-parameter foundation model that trained on 2.5 trillion tokens, is more domain flexible and less reliant on external API-based solutions. Experimental analysis of document question answering depicts that GraphRAG performs better by 23% and 46% in exact-match accuracy and multi-hop reasoning accuracy in comparison to baselines with only a slight augment in mean latency of 45 ms. The accuracy on complex queries of a schema-aware T2S model containing safety checks at execution of 94.2% is significantly greater compared to direct prompting on Trustworthy Language Model (TLM). The multi-agent model achieves 96.8% correspondence with professional recruiter guesses on 500 resumes and mean error of absolute lesser than that of uni-agent benchmarks. The independent research assistant saves approximately 65% time utilized in manual research and the level of accuracy is 98%.