Referral System

Overview

The Arkada Referral System rewards contributors who bring active users to the platform — users who complete quests and meaningfully participate.

Arkada is built on a simple principle: quest completions are the single core signal of value.

Referral rewards are distributed only when real participation happens, not based on reach, hype, or promotional effort alone.

The system is structured into distinct layers, each rewarding a different type of contribution.

---

User & Creator Referral Layers

These layers are designed for individual users, creators, influencers, and community leaders.

• Direct Referrals (Layer 1)

  Reward contributors who onboard users that actively complete quests.

• Mentorship Rewards (Layer 2)

  Reward contributors who onboard and support other contributors, stacking on top of direct referrals.

• Collaborative Pools (Layer 3) (coming soon)

  Enable groups of contributors to earn collectively through shared performance and coordinated execution.

All user-facing layers build on the same performance-based foundation and are subject to system-wide caps and safeguards.

---

Partner Campaign Structures

These structures are designed for projects, companies, and ecosystems using Arkada as an engagement infrastructure.

• Featured Projects

  Individual projects running dedicated quest campaigns with campaign-specific parameters.

• Ecosystems

  Large networks of projects coordinating quests and distributing rewards through shared prize pools.

These structures operate at a different scale and follow campaign-specific rules.

---

Rewards across the system are calculated using transparent, performance-based rules, with clearly defined caps and safeguards to ensure fairness, sustainability, and long-term alignment as the platform evolves.

Every contributor can track their activity, rewards, and progress through their profile page, which also provides access to referral links and detailed performance data.

In short:

If you bring users who complete quests, the system works for you.

Use the sections below to explore each layer in detail →