ProfitPulse AI: Multi-Agent Business Intelligence and Decision Support Platform for SMEs

Small and medium-sized manufacturers, wholesalers, and distributors often manage sales, inventory, financial, and supply-chain information across disconnected spreadsheets and business systems. This fragmentation makes it difficult to understand profitability, identify operational issues, monitor business performance, and make timely decisions. Traditional dashboards provide historical metrics but often lack the contextual intelligence needed to explain why performance changes and what actions businesses should consider. ProfitPulse AI addresses this challenge by bringing business intelligence and AI-assisted decision support into a unified platform.

Developed ProfitPulse AI, a full-stack AI-powered business intelligence and decision-support platform. The application uses a React and TypeScript frontend, a Python FastAPI backend, and PostgreSQL for structured business data. Its AI intelligence layer incorporates LangGraph orchestration and a two-stage analytical workflow: 1. Data Analyst: Processes structured business information and produces deterministic analytical results. 2. Business Analyst: Interprets analytical findings and generates contextual business insights and recommendations. The platform combines business dashboards, financial intelligence, product and regional analysis, inventory and supply-chain visibility, document retrieval through a RAG foundation, and a natural-language Ask ProfitPulse interface. The demonstration environment uses synthetic wholesale and retail business data. The solution is designed to help SMEs consolidate business information and obtain more accessible, evidence-based decision support.

The delivered prototype establishes a unified business intelligence experience across financial performance, sales, inventory, and supply-chain information. Key implementation outcomes: - Consolidated four business intelligence areas into one application. - Implemented a two-stage analytical workflow separating deterministic data analysis from AI-generated business interpretation. - Added an AI-assisted interface for asking business questions and retrieving contextual insights. - Established a RAG foundation for document-based knowledge retrieval. - Created a modular architecture that can be extended toward manufacturing-specific analytics and operational intelligence. The current demonstration uses synthetic data. Production productivity gains, financial savings, accuracy improvements, and customer adoption have not yet been independently measured.