NEURA — Traction

ROADMAP

MISSION COMPLETED

H1
2026

MVP Launch and Validation

Goal:

Launch beta, collect retention data, validate product-market fit

Retention metrics:

Retention >30% = success

SYSTEM ACTIVE

H2
2026

Engagement Economy

10 000 active users
Convert XP into NEURA's internal currency
Roll out internal marketplace
Deepen game mechanics

Q1
2027

WEB-3 Decentralization

Launch premium features and microtransactions
Mainnet launch of NEURA
Integration with staking and governance
Listings on major exchanges

Q2
2027

NEURA & AI Integration

Launch VIP tracker
AI assistant for personal analytics and micro-challenges
Rewarding Neura users for helping each other and contributing to the network's development

Design and
User Experience

Every interaction is intentional. NEURA's interface was crafted to feel effortless — fluid transitions, zero friction, and an aesthetic that rewards curiosity. Design isn't decoration here. It's the product.

Foundation
for AI Agent
Participation

Laying the groundwork for a truly intelligent ecosystem. NEURA provides the robust infrastructure required for autonomous AI agents to seamlessly integrate, assist with personal analytics, and actively participate in the new digital economy.

Multiplayer

The future of productivity is collective. NEURA's multiplayer mode lets teams move as one — building, iterating, and evolving in a shared space where ideas collide and compound in real time.

Proprietary Physics &
Graph Engine

Built on a custom graph rendering and physics architecture, Neura enables users to visualize complexity, coordinate AI agents, and manage progress through a dynamic, self-organizing ecosystem.

140 users
on the waiting list.

From zero to 140 users before the public launch. Word of mouth is a clear signal. Every person on this list chose NEURA because they believe in what lies ahead. The revolution won't wait.

Optimizing Efficient Context
Management for AI Assistants

Neura parses your entire codebase and documentation to build a structured, decentralized task and knowledge graph. It provides AI coding assistants with a highly efficient map of your project's architecture and dependencies. This approach drastically reduces token usage and improves contextual understanding without requiring developers to manually feed unorganized files into prompts.