Exa

Knowledge Systems: The New GTM Stack — Jeffrey Wang, Exa

Aug 26, 2026 · 18:49

Jeffrey Wang, cofounder of Exa, argues go-to-market is an AI engineering problem and details Exa's stack: an ICP dashboard classifying nearly every company in its addressable market with anticipated spend, and Request Lens alerting on meaningful customer events. The team runs about a dozen Slack agents plus Jeffbot, an AI clone of Wang trained on 760 emails—he averages 18 words and signs 'best' not 'sincerely'—and limited to drafts when others use it. He closes on three principles: agent-first means API-first, not everything should be a chatbot, and buy-versus-build is false—Salesforce exposed as MCP is arbitrarily customizable. He also notes an eight- or nine-person FDE org runs deals and builds the sales systems.

Building a Smarter AI Agent with Neural RAG - Will Bryk, Exa.ai

Jul 29, 2025 · 18:42

Will Bryk, CEO of Exa.ai, argues that traditional keyword search engines like Google are ill-suited for AI agents, which need semantic understanding, complex multi-paragraph queries, and comprehensive results. He explains Exa's neural search approach using embeddings to capture ideas and context, unlike keyword-based systems that miss nuances like negation. Bryk demonstrates a live-coded agent combining neural searches (e.g., finding personal sites of SF engineers who like information retrieval) with keyword searches (e.g., retrieving specific GitHub profiles), showing how Exa's API exposes toggles for date ranges, domains, and result counts. He emphasizes that AI agents require search engines that return exactly what they ask for, not what humans click on, and can handle thousands of results. The episode also previews Exa's new research endpoint for automated deep research.