Recommendations analyze your architecture and business requirements through a sophisticated multi-agent AI system to generate targeted recommendations.

How It Works

Catio analyzes two primary inputs to generate recommendations:

1. Stacks Module Data: Information about your current architecture and historical tech investments
2. Context Module Data: Details about your business goals and constraints

Using these inputs, Catio employs a multi-agent AI system that represents both Architects and Employees as AI agents working together to generate highly personalized recommendations.

Types of Recommendations

Recommendations vary in scope and complexity:

• Single atomic actions (e.g., swap database X for database Y)
• Sequential related actions
• Coarse-grained architectural changes (e.g., implementing a data warehouse)
• Composite actions that decompose into smaller recommendations

Structure of a Recommendation

Each recommendation contains three components:

Target Architecture

Defines the optimal architecture state:

• Proposed target state for your system
• Technical rationale for the recommendation
• Expected benefits from implementation