Last updated: 2026-05-26
Hyper is a YC-backed AI context layer that silently synthesizes company tools into a shared brain, plugging knowledge into Claude, Cursor, and Codex via MCP.
Hyper is a YC Spring 2026-backed AI context platform built by a 2-person team in San Francisco. It silently aggregates company data from 160+ tools including Notion, Slack, email, and GitHub, then injects that context into Claude, Cursor, Codex, and ChatGPT via an MCP server on every chat turn. Free during early access; pricing not yet publicly disclosed.
Hyper is an AI context infrastructure tool built by Shalin Shah and Kanyes Thaker, both UC Berkeley graduates and former Matic Robots engineers, backed by Y Combinator's Spring 2026 batch. It addresses a problem every AI-first team hits: every time you switch AI tools or start a new conversation, the AI has no memory of your company's context, decisions, or current state. The result is constant context pasting, repeated explanations, and inconsistent outputs across Claude, Cursor, Codex, and ChatGPT. Hyper solves this with background agents that silently read every update in your company's tools, including Notion documents, Slack messages, GitHub pull requests, emails, calendar invites, and CRM entries. Agent-driven algorithms then synthesize, deduplicate, and clean all of that information into a real-time shared knowledge base. That knowledge base is then injected into every AI tool your team uses via an MCP (Model Context Protocol) server, so Claude, Cursor, Codex, and ChatGPT all receive the same accurate company context on every chat turn without any manual prompting. The platform is best suited to AI-first teams of 2 to 50 people who run multiple AI coding, writing, or research tools in parallel. Engineering leads and CTOs benefit most: instead of every engineer pasting the same context about architecture decisions or sprint goals into their AI tools, Hyper distributes that knowledge automatically. Founders and operations managers also benefit when they need consistent AI outputs across functions without enforcing a specific prompt library. Hyper supports 160+ integrations via one-click OAuth or API key authentication, covering Gmail, Slack, HubSpot, Shopify, Notion, GitHub, and more. As of May 2026, the platform is in early access with pricing not yet publicly announced. The founding team brings strong product and robotics engineering credentials, and the two-person team has been friends since their first day at UC Berkeley. Because Hyper operates with broad read access to company communications and documents, teams should evaluate data handling and access controls carefully before connecting production systems. No public compliance documentation is available at this stage.
Early access available as of May 2026; pricing not publicly announced. YC Spring 2026 pre-seed stage. Contact team at kt@heyhyper.ai for access.
Hyper is an AI context infrastructure platform built by Shalin Shah and Kanyes Thaker, backed by Y Combinator's Spring 2026 batch. It runs background agents that silently read every update across your company's tools, including Notion, Slack, GitHub, email, and CRM, then synthesizes that data into a clean, deduplicated, real-time knowledge base. That shared company brain is then injected into Claude, Cursor, Codex, and ChatGPT via an MCP server on every chat turn. The result is that every AI your team uses automatically knows your current company context without any manual prompting. The platform is designed for AI-first teams of 2 to 50 people.
As of May 2026, Hyper is in early access and pricing has not been publicly announced. The platform is available for free during this phase, with access via the founding team at kt@heyhyper.ai. As a YC Spring 2026 pre-seed startup, Hyper will likely launch paid plans after the batch ends in mid-2026. Teams adopting Hyper during free early access should factor in potential cost transitions before building critical workflows on the platform. No free tier limits or credit caps have been documented publicly.
Hyper's three core features are silent context aggregation, agent-driven knowledge synthesis, and MCP-based AI injection. The aggregation layer reads Notion documents, Slack messages, GitHub pull requests, emails, and calendar invites continuously. Background agents then deduplicate and clean that data into a real-time shared brain. The MCP server then injects that knowledge into Claude, Claude Code, Cursor, Codex, and ChatGPT on every chat turn automatically, covering 160+ integrations via OAuth or API key connections.
Yes, Hyper is free during its early access phase as of May 2026. Access requires contacting the team rather than self-service sign-up. No credit card or usage limits have been documented publicly. As a two-person YC-backed pre-seed startup, the free phase will almost certainly transition to paid plans once the product exits early access. Teams should treat the current free period as an evaluation window rather than a permanent pricing model.
The closest alternatives to Hyper in 2026 are Notion (for shared company knowledge bases), Glean (for enterprise AI search across company tools), and Guru (for curated company knowledge cards). Unlike Hyper, these tools require manual curation and do not inject context directly into AI tools via MCP. For teams specifically wanting context injection into coding AI tools, Pieces for Developers and Mem0 offer agent memory capabilities but with a different integration model than Hyper's MCP approach.
Hyper is best for AI-first teams of 2 to 50 people who use multiple AI tools like Claude, Cursor, and Codex daily and spend significant time copy-pasting context between sessions. Engineering leads managing distributed teams benefit most from a shared real-time knowledge base that every AI tool accesses automatically. Hyper is not appropriate for teams with strict data security requirements, regulated industries handling sensitive data, or individuals who use only one AI tool, as the multi-tool context injection is its core value proposition.
Hyper is MCP-native, meaning it ships an MCP (Model Context Protocol) server that works with Claude, Claude Code, Codex, ChatGPT, and any MCP-compatible client. This is the primary integration interface for injecting company context into AI tools. A traditional REST API for developers is not publicly documented as of May 2026. The 160+ tool integrations use OAuth or API key authentication on the input side for data aggregation.
Notion and Hyper both store company knowledge, but they take fundamentally different approaches. Notion is a structured workspace where teams manually write, organize, and update information; it does not automatically inject that knowledge into AI tools. Hyper automatically aggregates knowledge from Notion and 159 other tools, then pushes it into Claude, Cursor, and Codex via MCP without any manual prompting. Notion is the better choice for teams that want a centrally managed, human-curated knowledge hub. Hyper is the better choice when you want that knowledge to automatically enrich every AI conversation your team has.
Hyper requires broad read access to email, Slack, GitHub, and CRM systems, which creates meaningful data exposure considerations. As of May 2026, the company has not published a SOC 2 report, EU AI Act compliance statement, or detailed data retention policy, which is expected for a 2-person pre-seed startup. Teams handling regulated data (healthcare, finance, legal) or sensitive client communications should wait for compliance documentation before connecting production systems. Smaller, less regulated teams can evaluate carefully by starting with lower-sensitivity tool connections like Notion or GitHub before connecting email or CRM.