Galactic is a conditioning treatment designed to restore ease, fluidity, and control to hair that feels resistant, tangled, or stressed.

This formula delivers immediate detangling and surface refinement, allowing hair to soften, align, and respond more predictably through cleansing and styling. Galactic supports smooth distribution from root to tip, helping hair feel more pliable, more manageable, and less reactive to manipulation.

Galactic prepares the hair for cleansing, reduces friction during shampooing, and improves overall handling — creating a refined starting point for care and styling routines.

Galactic supports hair that needs:

• Immediate detangling and reduced resistance
• Easier manipulation and even distribution
• Softer feel and smoother movement through wet and dry stages
• Improved manageability before cleansing and styling
• A conditioned, responsive surface without heaviness

Hair feels more cooperative, aligned, and easier to work with — supporting more controlled results throughout the routine.

Marshmallow Root Extract
Supports softness and detangling ease, helping hair release knots while maintaining flexibility and comfort.

Snow Mushroom Extract
Contributes lightweight hydration and surface smoothness, enhancing elasticity and refined movement without weight.

These elements work alongside Sizigē's conditioning architecture to deliver detangling that feels immediate, controlled, and elegant.

Galactic is for hair that benefits from added softness and ease — especially when detangling feels effortful or handling feels resistant.

It supports all textures and styles by improving how hair responds to manipulation, cleansing, and styling preparation.

• Apply to damp hair before cleansing, focusing on areas that need added softness and control.
• Distribute evenly and detangle gently.
• Proceed with shampooing as desired.

Galactic functions as a preparatory treatment that enhances performance across cleansing, conditioning, and styling systems.

It integrates seamlessly into Sizigē routines to support smoother transitions between steps and more predictable results.