Draper Associates has announced the establishment of its internal AI infrastructure: two years ago, portfolio data was scattered across 8 Excel sheets, and now it is managed by an automated main database that handles over 400 portfolio companies, more than 15,000 contacts, and over 1,900 meeting minutes, all searchable in everyday English on mobile devices.

The main database operates on the principle of one record per company, with all other processes writing to or reading from it. The tech stack is built on existing tools: Airtable, Zapier, Power Automate, Google Drive, Slack, and Claude, with self-built services hosted on Railway, avoiding new vendor contracts. Meeting minutes are automatically attached to company records after being transcribed by Otter, and pitch links are converted to PDFs for archiving via MCP services.

The self-developed Draper Brain serves as the MCP server, connecting Airtable, cloud storage, and authorized external data to Claude, covering about 450 portfolios and over 4,900 co-investors. When uncertain, the system pauses to ask a person, avoiding guessing in fields that must be correct. Lead scoring is determined by the fund's own themes and historical negatives, rather than using a generic scoring system.

The Deal Engine is trained on 40 years of investment and rejection records, reading hundreds of incoming projects weekly and emailing them to the investment team. After submission by founders, Zapier triggers web searches and generates introduction emails to be sent; usage of this introduction feature has increased about tenfold since its launch. Schedule tasks read from a read-only inbox, CRM, and portfolio updates to generate daily briefs, while fund financials are synchronized via Power Automate.

The article is authored by Michelle Kwok, Andrew Hyun, and Patrick Blumenthal. The fund operates with approximately $2.3 billion in assets under management and plans to launch a $300 million Fund 8 in 2026. The portfolio includes publicly known paths such as Tesla, Skype, and later Carta, ICEYE, Polymarket, etc. Tim Draper mentions that he developed judgment from projects that were initially rejected in 1985, and now machines handle routine searches.

Who buys is determined by the investment team's time and judgment bandwidth, while who sells is the internal engineering that productizes routine searches. Funds are not directed towards new software purchases but rather towards cleaning proprietary data and maintaining prompts. Beneficiaries are partners who can use natural language to access files and founders who are matched with introductions more quickly, while those under pressure are project parties that still rely on multiple table exports and cannot be indexed by the main database.

The system clearly leaves "sitting down to meet founders and make decisions" to humans. Each tool has documentation written based on already launched products, requiring the next person to be able to get started in five minutes.

Source: Public Information