Hyper is building what it calls a self-driving company brain, an always-on knowledge layer that learns from everything an AI-first team does and quietly makes every other AI tool in the stack smarter. Instead of asking employees to maintain a wiki or curate context for each model they use, Hyper watches the work itself: Notion docs, Slack threads, Claude Code questions, Cursor sessions, customer emails, LinkedIn DMs, and other surfaces where institutional knowledge actually lives.
Agent-driven algorithms then clean, deduplicate, and structure this stream into a real-time knowledge graph shared across the team. The magic is in how that knowledge flows back out. Rather than asking users to switch into a new chat interface, Hyper injects relevant company context into the AI tools employees already use, so every chat turn in Claude, ChatGPT, Cursor, or internal copilots is grounded in the latest team knowledge automatically. The result is compounding institutional memory: onboarding gets faster, repeat questions disappear, and AI agents stop hallucinating about the company's own product, customers, and decisions.
Hyper is part of Y Combinator's Spring 2026 (P26) batch and was founded by Shalin Shah and Kanyes Thaker, who previously worked together at Matic Robots and both studied at UC Berkeley. The team of two is based in San Francisco and is currently onboarding design partners ahead of broader availability. As an early-stage YC company, funding is at the standard YC investment level.