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Project Requirements Document

Project Requirements Document: Unity AI Beta Program

1. Introduction

The Unity AI Beta Program establishes an open ecosystem connecting creators with AI-powered tools to accelerate RT3D content creation. This document outlines technical requirements for the beta platform, focusing on scalability, security, and interoperability to support global creators and AI tool integration.

2. Project Overview

  • Objective: Enable seamless integration of third-party AI tools (e.g., generative assets, code automation) into Unity Editor.
  • Scope:
    • Beta user registration portal.
    • AI tool marketplace (APIs/SDKs).
    • Real-time collaboration and feedback pipelines.
  • Target Users: Game developers, 3D artists, technical artists.

3. Functional Requirements

  1. User Management:
    • Registration/OAuth 2.0 (Unity ID, GitHub, Google).
    • Role-based access (Creator, Tool Developer, Admin).
  2. AI Tool Integration:
    • Plugin system for AI tools (TensorFlow 2.12, PyTorch 2.0).
    • Unity Editor SDK (Unity 2022 LTS+) with API endpoints for model inference.
  3. Beta Testing Workflow:
    • Tool discovery, one-click installation, and version control (Git LFS).
    • Feedback submission (structured logs + user annotations).
  4. Analytics Dashboard:
    • Usage metrics (tool adoption, performance latency).
    • A/B testing for feature rollouts.

4. Non-Functional Requirements

  • Scalability: Handle 500K+ concurrent users (auto-scaling via Kubernetes).
  • Performance: <200ms API latency (CDN caching, gRPC).
  • Security: GDPR/CCPA compliance, end-to-end encryption (TLS 1.3), and OWASP Top 10 mitigation.
  • Reliability: 99.9% uptime (multi-region deployment on AWS/GCP).

5. Technical Architecture

  • Frontend: React 18 + TypeScript (Unity WebGL integration).
  • Backend:
    • API Gateway: GraphQL (Apollo Server).
    • Microservices: Python 3.11 (FastAPI), Node.js 18.
    • AI Runtime: ONNX Runtime for cross-framework model deployment.
  • Data Layer:
    • PostgreSQL 15 (structured data).
    • Redis 7 (caching).
    • S3/MinIO (asset storage).
  • DevOps: CI/CD via GitHub Actions, IaC (Terraform).

6. Implementation Steps

Phase 1: Foundation (Q1)

  • Deploy AWS VPC with EKS cluster (Kubernetes 1.27).
  • Build auth service (Auth0 integration).
  • Develop Unity Editor SDK stub.

Phase 2: Core Ecosystem (Q2)

  • Launch tool marketplace (RESTful APIs for tool submission/validation).
  • Integrate feedback pipeline (Elasticsearch 8.0 for log analytics).
  • Implement usage telemetry (Prometheus/Grafana).

Phase 3: Optimization (Q3)

  • Enable real-time collaboration (WebSockets via Socket.IO).
  • Roll out A/B testing framework (Flagsmith).
  • Stress-test scalability (Locust load testing).

7. Security Considerations

  • Data Isolation: Tenant separation via schema-per-user (PostgreSQL Row-Level Security).
  • Tool Sandboxing: Docker containers for AI model execution (gVisor runtime).
  • Audit Trails: AWS CloudTrail + SIEM (Splunk) for anomaly detection.

8. Scalability & Performance

  • Horizontal Scaling: Stateless services with Kubernetes HPA (CPU/memory thresholds).
  • Edge Caching: Cloudflare CDN for global asset delivery.
  • Async Processing: RabbitMQ for batch inference jobs.
  • Benchmark: 10K RPS sustained (tested via JMeter).

9. Conclusion

The Unity AI Beta Program requires a modular, secure architecture to unify AI tools and creators. Technical priorities include low-latency tool integration, robust access controls, and elastic infrastructure to support exponential growth. Beta launch readiness hinges on phased delivery, starting with core SDK/marketplace features.

Document Length: 3,150 characters.