Transcript AI: A Multilingual Business Dialogue Understanding System Based on Large Language Models and RAG

This article introduces the Transcript AI project, which aims to solve the problems of transcription and intent understanding of multilingual dialogues in international business scenarios. The system integrates Large Language Models (LLM) and Retrieval-Augmented Generation (RAG) technologies, breaking through the limitations of traditional transcription tools and realizing the leap from text recording to intent understanding.

In cross-border meetings and multilingual team collaboration, language barriers remain an efficiency bottleneck. Traditional transcription tools only mechanically record text and cannot capture the subtle meanings of context switches, expression deviations caused by cultural differences, and hidden business intentions. For example, in a trilingual meeting (Chinese, English, Japanese), traditional tools tend to lose contextual connections, leading to understanding deviations.

Multilingual business dialogue scenarios are complex: participants may mix languages, there are ambiguities in professional terms, and cultural backgrounds affect expressions. The traditional ASR+MT pipeline solution easily loses context, leading to 'correct translation but incorrect understanding'. Transcript AI redefines the task as 'context-aware multilingual understanding' rather than a simple speech-to-text mapping.

LLMs (such as GPT, Claude) play three core roles: 1. Context Integrator: Understand the complete dialogue context across language boundaries; 2. Intent Recognizer: Determine the business motives behind the surface text in combination with context; 3. Knowledge Activator: Call pre-trained knowledge to explain terms and identify processes. In addition, task decomposition through prompt engineering (language recognition → transcription → context alignment → intent extraction → summary) improves output quality and interpretability.

Pure LLMs have knowledge cutoff and hallucination risks. RAG mitigates these issues by combining external knowledge bases: 1. Real-time retrieval from enterprise knowledge bases: Access historical meetings, project documents, etc., to assist in understanding content such as 'refer to the Q3 plan'; 2. Domain term disambiguation: Retrieve definitions from enterprise glossaries to resolve ambiguities of polysemous words (e.g., ROI); 3. Dialogue history memory: Retrieve early fragments to maintain cross-time context consistency.

The basic layer uses multilingual ASR (e.g., Whisper) to handle code-switching, speaker separation to distinguish participants, and noise suppression to improve input quality. To achieve real-time performance, a sliding window mechanism is used to balance latency and accuracy; the RAG module optimizes vector database indexing to ensure millisecond-level responses. In the future, multimodal fusion can be expanded (combining OCR and visual understanding to process PPT and whiteboard content).

1. Real-time assistance for cross-border meetings: Provide real-time transcription and key point extraction for non-native speakers, reducing cognitive load; 2. Intelligent meeting minutes: Automatically generate structured summaries, mark decisions and action items, and improve post-meeting efficiency; 3. Compliance risk management: Mark speech fragments with compliance risks to assist manual review (applicable to regulated industries such as finance and healthcare).

Current challenges include limited support for low-resource languages, privacy and security (sensitive information requires local deployment and encryption), and personalized adaptation to enterprises' unique terms and habits. Future directions: Expand coverage of small languages, strengthen privacy mechanisms, and implement continuous learning to adapt to specific contexts.