Story

I built CoThou after seeing search and AI answer engines give completely incorrect information about my company. Turns out, they prioritize structured, citable content, so I reverse-engineered how they choose sources and built CoThou to become the source of truth.

How it works For businesses: Create a company profile. When search and AI answer engines are asked about your company, they’ll cite your company profile and its content, not Wikipedia or outdated info.

For publishers and knowledge workers: Publish at your personal profile with proper citations (300M+ academic papers indexed). When someone asks search and AI answer engines about your topic, it will cite your work linking to your profile and allowing citation tracking.

Try it now (unlimited during beta): → https://cothou.com

It’s v0.01 and rough around the edges. Try it and let me know what breaks.

What’s next: Currently training a custom 32B MoE (Mixture of Experts) LLM with 3B active parameters scheduled to go live in Q1/2026. The key difference: it breaks down complex queries into parallel subtasks that execute live on an infinite canvas. You’ll see agents plan and build in real time, instead of waiting for a progress bar.

Examples: “Write a 300-page book on the history of computing” “Create a 60-second TikTok ad for my SaaS”

It handles research, outlines, storyboarding, asset generation, voice-overs, and music simultaneously.

Since only ~3B parameters are active per token, it runs 8–10× cheaper and faster than dense 32B models, while still matching or outperforming premium models on reasoning, coding, and long-context tasks.

Building through partnerships with NVIDIA Inception and Microsoft for Startups.

Would love HN feedback on: - Improving citation accuracy - Building trust with AI parsers - What sources to add next (currently 100M companies + 300M academic papers) - Anything else

Marty (Founder)