Technological advancement in automobile production, regulations, sales, and services leads to extensive technical documents and manuals. However, lengthy documentation, language barriers make it challenging to find quickly, highlighting the importance of summarization to bridge this knowledge gap. The Large Language Models (LLMs) have demonstrated impressive capabilities in tasks like Q&A, coding, drafting, and scientific acumen, and are commonly used to condense many complex documents. Despite LLMs being widely used in various sectors like research and journalism, LLMs face challenges in reliably summarizing content. Their word-by-word tokenization can sometimes fail to capture the true meaning and context, especially for abstractive summaries. This paper explores Large Concept Models (LCMs), which operate on principles of semantic reasoning, cross-modality integration, and hierarchical structuring at a higher conceptual level. This makes LCMs language and modality agnostic, encapsulating a hierarchical flow of ideas. LCMs synthesize multiple sentences into a few concepts. The document is segmented into smaller text sequences based on grammatical patterns and nouns using the Segment any Text suite (SaT). Sentence segments are capped at a maximum length to preserve contextual information. These segments are fed into the SONAR embedding space, a language-agnostic system trained as an encoder/decoder, supporting text in over 200 languages and speech in 76 languages. A trained diffusion model generates outputs for sequential inputs, which are then processed by the SONAR decoder to produce human-readable text. This work lays the groundwork for future research in developing scalable, adaptive, and semantically aware AI systems for the automotive domain, potentially overcoming challenges associated with token-level processing in complex or multilingual scenarios. The approach aims to benchmark query answering efficiency and increase real-time response, representing a significant advancement in human-like understanding and intelligent automation for vehicle data handling