Mobile sandboxes for your AI agents controlled over REST API and MCP
AI model of your product's business logic accessible to people and AI agents
Stage
Not given
Not given
Founded
Not given
Not given
Based in
US
GB
Pricing model
Paid
Freemium
Pricing
Cloud Android 14: $0.01 per phone-minute; iPhone and iPad: $0.05 per phone-minute; Real Android devices: $0.03 per phone-minute; Dedicated devices: from $79 per month. Billed per second with one-minute minimum. Boot time and parked phones free. New accounts start with $2 credit.
Free: 10,000 starting credits plus 1,000 per month. Engineering: $29 per active developer per month with 30,000 pooled credits each month. GitMir Vibe: $9 per person per month. Cloud Compute: $99 per project per month. MeetAI: $49 per project per month. GoBots: $99 per venture per month. Market: $49
Key features
Cloud Android 14 phones (arm64)
iPhone and iPad sandboxes
Real Android devices
REST API and MCP server
Screen as numbered text elements
Batch actions (tap, type, swipe up to 20 per call)
Park and resume phones with state preserved
Live view with step-by-step recording
App installation and management
Screenshot and text observation modes
Builds features, rules and customer paths and keeps them current with every push
Shows what each commit changed in the business logic, not only in the code
Provides change impact analysis before merge showing what could break
Checks PRDs and specifications against the product
Delivers understanding to AI agents over MCP
Runs locally on your machine with GMM Local
Finds risky patterns and problems in the product
Enterprise deployment inside your security boundary
Integrates with Claude Code, Cursor, Codex and VS Code
MCP access for agents with 1-10 credits per answer
What makes it different
Same API and MCP for Android, iPhone, iPad and real devices
Agents interact with real apps like people do
Boot time and parked phones are free
Works with Claude Code, Codex, Cursor and any HTTP MCP client
Nearly 3× fewer errors on Benchmark 001 compared to alternatives
8.4K tokens per question instead of 27.4K for competitors
Results published and measurable; method is proprietary
Reduces AI cost per completed agent action from $0.35 to $0.08