Hyper is an enterprise AI coding platform that replaces outsourced development by enabling teams to build systems of record, context, and autonomous agents internally. The platform generates core schemas and uses a Co-Pilot to structure service interfaces, with MCP support ensuring AI assistants like Claude and Cursor can safely complete coding tasks. It offers both Co-Pilot Assisted and Studio-Driven modes, giving developers flexibility while producing portable, production-ready code they fully own.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises often rely on outsourced development teams or vertical SaaS vendors to build custom software, which creates dependencies, limits flexibility, and results in code they don't fully control. Low-code tools also fail when teams need custom logic or specific frontend tooling, forcing developers into rigid paths that break their architecture.
Solution
Hyper is an enterprise AI coding platform that enables a self-build culture where engineering teams retain judgment while AI accelerates execution. After Hyper generates a core schema, its Co-Pilot helps structure service interfaces and boundaries that AI coding assistants can understand and build upon without breaking the architecture. The platform standardizes contracts, auth, and service boundaries to ensure MCP readiness, allowing AI assistants like Claude and Cursor to safely complete the last mile of coding. Developers can choose between Co-Pilot Assisted mode for speed and scaffolding or Studio-Driven mode for full visual control over schemas and workflows, resulting in portable, production-ready code the team fully owns.
Target Audience
Primary customers are enterprise engineering teams and developer-led organizations seeking to replace outsourced development with internal AI-accelerated coding while maintaining architectural control.
Features
- Co-Pilot Assisted mode that analyzes needs, recommends architectures, and configures services automatically for rapid scaffolding
- Studio-Driven mode providing full visual control for precise schema and workflow configuration in complex logic design
- MCP enablement that standardizes contracts, auth, and service boundaries so AI assistants can safely complete coding tasks
- Service interface structuring that makes code understandable and actionable for AI coding assistants without breaking architecture
- Generation of portable, production-ready code that teams fully own, avoiding vendor lock-in
- Support for building systems of record, systems of context, and autonomous agents that execute real work