CausalityAI

We’re building a system that helps people understand how real-world events actually unfold. Most tools today focus on summarization — they tell you what happened. But when you’re trying to understand something complex, like a company’s rise, a market crash, or a major product decision, summaries are not enough. The real value is in the sequence of events that led there. Our system takes a different approach. A user starts with an outcome — for example, “How did Google become successful?” or “Why did this company fail?” — and the system works backwards. It uses web-scale search to gather relevant information from across the internet, then applies language models to extract and organize the key signals, decisions, and events that contributed to the outcome. Instead of isolated facts, it builds a connected chain of causes. The result is a structured view of an event: not just a description, but a map of how it formed over time. Early signals, turning points, and key decisions are all surfaced and linked together into a coherent sequence. Right now, the system is already working end-to-end: search, ingestion, and causal extraction are all functional. The next steps involve improving quality, moving from snippets to full-page analysis, and building a proper timeline visualization layer. Our long-term goal is simple: take any meaningful outcome on the internet and make its underlying chain of events easy to trace, inspect, and understand.