Hardbusiness e-commerce retail technology

Pre-Sales Research and Competitor Analysis Automation Agent

Added Jun 2025 3 design docs

Sales teams lose hours before every pitch to manual research on prospects, competitors, and market positioning, and the quality of that research varies with whoever had time to do it. This project builds an agentic AI system that does the legwork automatically and consistently. The intern develops agents with Langchain that plan and execute research tasks, using LlamaIndex to ingest and query documents and OpenAI models for analysis and synthesis. The system gathers prospect and competitor information, evaluates positioning, and produces strategy recommendations that sales teams in e-commerce and retail can act on directly. A Flask backend exposes the agent workflows as services, while a Streamlit interface lets users launch research runs, watch progress, and explore the resulting insights interactively, with the whole system implemented in Python. The project takes the intern beyond calling a language model into genuine agent engineering, including decomposing a business workflow into autonomous steps, grounding outputs in retrieved data, and packaging the result as a usable product. Completing it demonstrates practical, current AI engineering applied to a problem with clear commercial value.

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