Automated Pre-Sales Competitor Research Agent for Sales Teams

Added Jun 2025 3 design docs

Before any serious sales call, account executives are expected to know the prospect's business, the competitors already courting them, and the angles most likely to land. In practice that research is done manually, minutes before the meeting, or skipped entirely, and deals are lost to better-prepared rivals. Sales and business development teams in retail and B2B settings need that preparation automated and standardized. The intern builds an agent that performs this pre-sales research on demand. A Flask application written in Python accepts a prospect or market segment as input, then uses LangChain to orchestrate a multi-step pipeline: gathering competitor and market data, extracting the facts that matter for a sales conversation, and calling OpenAI models to turn raw findings into a concise, actionable briefing with talking points and risk flags. The intern designs the chain structure, the prompts that keep outputs factual and sales-relevant, and the endpoints through which a sales team would request and retrieve briefings. By finishing the project the intern demonstrates agentic workflow design, LangChain chain composition, careful prompt engineering for business-critical output, and the ability to wrap an AI pipeline in a deployable Flask service that solves a measurable commercial problem.

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