Multilingual and Multimodal Business Intelligence Agent: A VoiceDriven Analytics System for Handwritten, Printed and Digital Business Records

Abstract

Small and medium businesses generate valuable transactional data, yet much of it remains locked in handwritten registers, paper bills and phone photographs, and the analytics tools that could exploit it presuppose structured data, technical skill and English language interaction. Recent advances in transformer based optical character recognition (OCR), Indian language speech recognition, large language model (LLM) agents, retrieval augmented generation (RAG), timeseries forecasting and anomaly detection make it plausible to remove these barriers, but the techniques have largely been studied in isolation. This paper reviews seven enabling technology areas—document digitization, multilingual and codeswitched speech, L-driver text to code analytics, forecasting and anomaly detection, retrieval and memory, data validation and explainability, and voice driven inventory automation—and compares representative methods against the needs of nontechnical Indian shop owners. Building on this review, we propose MMBIA, a layered reference architecture for a Multilingual and Multimodal Business Intelligence Agent that integrates sixteen functional modules around a validation first, human in the loop, local first design, together with an evaluation framework and a catalogue of open research problems. The architecture is a design proposal; no empirical performance claims are made.

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