Runtime security layer that stops risky AI actions before they execute
The native language of AI; in human terms, a protocol.
Stage
Not given
Startup
Founded
Not given
2026
Based in
Not given
CA
Pricing model
Freemium
Open source
Pricing
Free plan: Local Guard runtime with real-time approval prompts and local audit log. Solo $4.99/mo: adds 2 cloud-connected devices and 30-day cloud retention. Pro $15/mo: adds 5 cloud-connected devices, 180-day retention, and policy versioning. Team $30/seat/mo: up to 25 cloud-connected devices, 365-
Not given
Key features
Surface-specific pre-action interception
Policy routing with allow, observe, ask, and block outcomes
Local review and evidence workflows
MCP, skill, plugin, and package inspection
Layered local and team policy
Open source runtime under Apache-2.0
Offline local protection without cloud connectivity
Continuous GitHub leaked-secret monitoring
Decision audit trail and export
Support for multiple AI coding agents
AI-native communication protocol with 88 verbs and two syntaxes
AI See: give any AI model vision by pasting i.ilang.ai URLs
Zero installation required, pure text format
Works across ChatGPT, Claude, Gemini, DeepSeek, Qwen and other models
Loader available via pip and npm for automatic protocol injection
Prompt compression: 67% reduction in tokens for typical instructions
What makes it different
Open-source core runtime runs locally without cloud requirement
Protects against secrets, destructive commands, prompt injection, and supply chain attacks
No source code or secrets uploaded; decisions stored as redacted summaries
First protocol to formally map Greek mathematical symbols as primitive verbs for AI-to-AI communication
Computable vector space for AI judgment using 11 dimensions and fuzzy mathematics instead of binary classification
Open source and MIT licensed with no royalties or approval required
Operates at the instruction and behavioral layer, complementary to MCP and A2A protocols
Tested and benchmarked with public conformance results and DOI-registered research