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HyperRelease

HyperRelease vs Runway

Runway automates the mobile release engineering cycle. HyperRelease is agentic-first: your AI agent prepares store copy via MCP, then you publish to both stores.

Need release notes and listing copy ready without living in App Store Connect? HyperRelease is agentic-first: your AI agent drafts iOS and Android store copy via MCP, you review in one workspace, and HyperRelease publishes to both stores. Runway is built for a different job — automating the engineering release cycle around builds, CI, and rollouts.

What Runway does well

Runway sits across your mobile toolchain — GitHub, Bitrise, Jira, Linear, App Store Connect, Google Play — and automates much of the mobile release cycle: branch cuts, version bumps, ticket reconciliation, build distribution, and rollout health. For mobile engineering teams that want releases to run with minimal manual orchestration, Runway is a mature, purpose-built option.

What HyperRelease is built for

HyperRelease is the release workspace your agent operates. Claude, Cursor, Codex, or OpenClaw create the version, fill per-locale What's New and Play release notes, and mark platforms ready through MCP and the API. Humans review status in the console; HyperRelease propagates listing copy to App Store Connect and Google Play. It does not upload builds, automate branch cuts, or monitor rollout metrics — that stays with tools like Runway or your CI.

At a glance

CriteriaRunwayHyperRelease
Agentic workflowNot agent-nativeAgentic-first — MCP, API, and installable skill
Primary focusMobile release engineering automationAgent-prepared store copy and release readiness
PlatformsiOS and AndroidiOS and Android listing copy
Build and CI integrationDeep — automates pipeline stepsNone — complements your CI/CD
Store contentApp store management within mobile flowAgent drafts once; HyperRelease publishes to both stores
Rollout monitoringBuilt in — halt, accelerate, roll backNot in scope
Typical buyerMobile engineering and release opsFounders and small mobile teams using AI agents

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HyperRelease documentation

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