Context Capsules™: Privacy-Preserving Code Analysis at Scale

We represent code as AST + symbol tables with dependency metadata. Capsules exclude raw source and are encrypted in transit.

Architecture Overview

• Ephemeral by design
• AES-256-GCM with per-session keys
• Supports inline policy signals

Context Capsules™ enable us to analyze and transform code without ever storing or transmitting the original source code. This privacy-preserving approach is essential for enterprise customers who cannot share their proprietary code with external AI services.

How Context Capsules Work

Instead of sending raw source code to our AI models, we extract the structural and semantic information needed for analysis:

// Original source (never transmitted)
function calculateInterest(principal, rate, time) {
  return principal * rate * time / 100;
}

// Context Capsule representation
{
  "ast": {
    "type": "FunctionDeclaration",
    "id": "node_123",
    "params": ["param_1", "param_2", "param_3"],
    "body": {
      "type": "ReturnStatement",
      "argument": {
        "type": "BinaryExpression",
        "operator": "/"
      }
    }
  },
  "symbols": {
    "param_1": { "type": "number", "usage": "multiplication" },
    "param_2": { "type": "number", "usage": "multiplication" },
    "param_3": { "type": "number", "usage": "multiplication" }
  },
  "metadata": {
    "function_complexity": 2,
    "dependencies": [],
    "patterns": ["mathematical_computation"]
  }
}

This approach allows our AI models to understand code structure, identify patterns, and generate transformations while maintaining complete privacy.