The Hog

The Hog is building the AI-native Go-To-Market Operating System.

We're eliminating the manual research burden that slows down sales and marketing teams. Our AI agent monitors industries, competitors, and trends 24/7, then automatically surfaces prioritized opportunities with ready-to-execute multi-channel campaign strategies.

The problem: B2B teams use several disconnected tools for competitive intelligence, market research, campaign planning, and execution. This fragmentation makes it nearly impossible to develop holistic GTM strategies quickly or connect insights to action.

Our solution: An AI-powered operating system that consolidates GTM intelligence and strategy into one platform. We help small teams compete with larger organizations by multiplying their impact through intelligent automation.

Early traction: 100 paying customers and counting.

Why join: You'll work with a world-class team that has built frontier products at both startups and enterprises. Our team has deep GTM expertise, having scaled a company from $900K to $7M ARR in under 3 months. Our CTO has previously led AI evals for enterprise and startups, with customers at their former employer claiming the agents he developed were better than Glean's agents (unicorn competitor). If you want to work on AI that directly impacts revenue and helps businesses punch above their weight, this is it.

Tech Stack

• Frontend: Next.js, TypeScript, Tailwind CSS, shadcn/ui, Radix UI
• Backend: Supabase (PostgreSQL), Clerk.dev (auth), Trigger.dev v4 (background jobs), Vercel AI SDK
• AI/ML: Anthropic Claude, Google Gemini, OpenAI
• Data Pipeline: Web scraping, third-party data APIs
• Infrastructure: PostHog (analytics), Sentry (error tracking), Stripe (billing)

Hard Problems We're Solving

High-Volume Postgres
Our product can generate millions of Postgres records within just a few days of usage. We’re designing schemas, indexes, and query patterns to keep performance fast and stable under this growth.

LLM-First Product Surface
We’re building end-to-end flows around tool-calling LLMs, background jobs, and external data so users get concrete GTM recommendations instead of raw model outputs.