Dossier

Why I built this

Writing investment memos is slow. Each one requires founder background research, competitive landscape analysis, market sizing, and synthesizing it all into a structured document — easily a full day's work per deal. With hundreds of inbound deals, the team couldn't write memos for every promising one. I built Dossier to run the research pipeline in parallel and produce a first-draft memo automatically, so the team could spend their time refining analysis instead of gathering it.

What it does

Dossier takes raw deal data and call notes and produces structured, IC-ready investment memos. A deep research pipeline runs in parallel — founder background, competitive landscape, market sizing — then synthesizes everything into a 9-section memo and pushes it to Notion.

Research pipeline

Before memo generation, four research strands run concurrently:

• Founder research — LinkedIn background, prior companies, domain expertise
• Competitive research — market players, positioning, differentiation signals
• Market sizing — LangGraph multi-step agent using Tavily + Perplexity + LLM synthesis
• Synthesis — strands merged into a thesis draft with a contrarian take, fed to the memo generator

Memo structure

Stack

Hono (Node.js backend), Next.js (frontend), OpenAI, LangGraph, Tavily, Perplexity, Notion API, Airtable, pnpm workspace