From specs to shipped
Deploy to agents, experiment, and track what ships. Hand off specs to AI coding agents, preview with your team, run A/B tests, and build institutional memory from outcomes.
Agent Dispatcher
Bridge the last mile from spec to implementation
Transform approved specs into optimized outputs for Cursor, Claude Code, Jira, Linear, and more. Zero-ambiguity handoff with full context.
Agent-optimized prompts
Structured JSON prompts designed for AI coding assistants—not verbose prose documents.
Full context included
Every export includes technical context, acceptance criteria, and codebase hints.
Bidirectional sync
Track implementation progress in Jira or Linear with automatic status updates.
Multi-format export
Export to Jira, Linear, GitHub Issues, or as structured agent prompts.
Test case generation
Auto-generate test cases from acceptance criteria in any framework.
PR template creation
Generate pull request templates with linked requirements and checklists.
Drift detection
Alert when implementation diverges from the original spec.
Preview Portal
BetaShip day without anxiety
Sandbox environment to preview agent-coded changes, run collaborative dogfood sessions, collect feedback, and deploy with confidence.
Visual code review
See what changed without reading code—side-by-side comparison of production vs. sandbox.
Collaborative dogfooding
Invite team members to explore together with real-time cursors and annotations.
Staged deployment
Roll out to 1%, 10%, 50% of users with guardrail monitoring and one-click rollback.
Sandbox configuration
Set feature flags, tenant context, and user roles for realistic previews.
Annotation tools
Draw, comment, and record screen flows directly on the preview.
Auto-prioritized feedback
Collected feedback is ranked by severity, frequency, and source role.
Agent refinement prompts
Generate structured instructions for coding agents from feedback items.
Experiment Console
BetaValidate hypotheses with zero engineering overhead
Set up A/B tests without engineering. AI suggests audiences, syncs feature flags, and monitors metrics automatically.
No-code setup
Configure experiments through a visual interface—no engineering tickets required.
Smart audiences
AI suggests optimal test audiences based on your customer segments and goals.
Integrated results
Learnings automatically feed back into your evidence base for future decisions.
Feature flag sync
Connect LaunchDarkly or Statsig for automatic flag management.
Metric tracking
Define success metrics and monitor statistical significance in real-time.
Audience targeting
Target by customer segment, usage tier, or custom attributes.
Results analysis
Automatic statistical analysis with recommendations for next steps.
Outcome Ledger
From ship-and-forget to continuous learning
Connect shipped features to measurable results. Surface historical context when similar opportunities arise. Never repeat the same mistake.
Automatic checkpoints
System prompts for outcome data at 7, 30, 60, and 90 days post-launch.
Historical context
When evaluating new opportunities, see similar past initiatives and their outcomes.
Institutional memory
Learnings persist across team changes—new PMs inherit full decision history.
Metric connection
Link shipped features to Amplitude, Mixpanel, or custom metrics.
Similarity detection
AI surfaces related past initiatives when you're planning something similar.
Failure warnings
Proactive alerts when you're approaching a known pitfall.
Portfolio analytics
See success rates, prediction accuracy, and learning patterns across features.
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