AI selection architecture document

AI Selection Architecture Document: Unity AI Beta Program

Version: 1.0
Date: October 26, 2023

1. Introduction

The Unity AI Beta Program establishes an open ecosystem connecting creators with AI tools to accelerate real-time 3D (RT3D) content creation. This architecture enables secure, scalable integration of AI capabilities into Unity workflows, supporting global scalability and phased beta testing.

2. Architecture Goals

• Interoperability: Seamless integration with Unity Editor (2022.3 LTS+) and Unity Cloud.
• Scalability: Support 1M+ creators and 10B+ user interactions.
• Security: GDPR/CCPA compliance, end-to-end encryption, and RBAC.
• Extensibility: Modular design for third-party AI tool onboarding.
• Performance: <100ms latency for AI inference APIs.

3. High-Level Architecture

• Frontend: Unity Editor Plugins (UI Toolkit) + Web Portal (React 18).
• AI Gateway: API gateway (Kong 3.4) routing requests to AI microservices.
• AI Microservices:
  • Training Service: PyTorch 2.0 + Kubeflow 1.7 for model fine-tuning.
  • Inference Service: TensorFlow Serving 2.12 or ONNX Runtime 1.15.
  • Tool Registry: Central catalog of AI tools (Elasticsearch 8.9).
• Data Layer:
  • Metadata: PostgreSQL 14 (creator profiles, tool metadata).
  • AI Assets: S3-compatible object storage (MinIO for beta, AWS S3 for prod).
• Orchestration: Kubernetes 1.27 (EKS/GKE) + Helm for deployment.

4. AI Tool Selection Criteria

Key Considerations:

• Prefer containerized tools for Kubernetes orchestration.
• Tools must expose REST/gRPC endpoints for integration.
• Prioritize ONNX support for cross-framework portability.

5. Implementation Steps

Phase 1: Foundation (8 Weeks)

1. Setup Infrastructure:
   • Deploy Kubernetes cluster (EKS) with auto-scaling node groups.
   • Configure Kong API Gateway with OAuth2.0 plugin for auth.
2. Integrate Core Services:
   • Connect Unity Editor to AI Gateway via gRPC.
   • Implement RBAC using Auth0 (custom roles: creator, beta-tester, admin).
3. Onboard Baseline AI Tools:
   • Containerize Unity Muse for model serving.
   • Configure OpenAI Codex with prompt-injection safeguards.

Phase 2: Beta Rollout (4 Weeks)

1. Data Pipeline:
   • Ingest anonymized usage data via Apache Kafka 3.4 → BigQuery.
   • Apply tokenization for sensitive inputs (e.g., game scripts).
2. Beta Management:
   • Use LaunchDarkly (v8.0) for feature flags and A/B testing.
   • Deploy feedback portal (Sentry + Jira Service Desk).

Phase 3: Scaling & Extensibility (Ongoing)

• Add tool submission SDK for third-party developers (Python/TypeScript).
• Implement CI/CD for AI models using MLflow 2.3.

6. Security & Compliance

• Data Isolation: Per-creator S3 buckets with IAM policies.
• Model Security: Scan custom models for vulnerabilities (Snyk Container).
• Audit: All API calls logged via AWS CloudTrail + ELK Stack.
• Compliance: Encrypt PII at rest (AES-256) and in transit (TLS 1.3).

7. Performance & Scalability

• AI Inference:
  • GPU-accelerated nodes (NVIDIA A10G) for heavy workloads.
  • Cache frequent requests using Redis 7.0 (e.g., common texture prompts).
• Scaling Triggers:
  • Horizontal pod autoscaling (CPU >70% or latency >80ms).
  • Geo-replicated clusters in AWS us-east-1, eu-central-1, ap-northeast-1.

8. Extensibility Roadmap

• Q1 2024: Support ONNX model marketplace.
• Q2 2024: Integrate Unity Sentis for embedded model execution.
• Q3 2024: Add reinforcement learning agents for gameplay testing.

9. Conclusion

This architecture leverages cloud-native technologies and modular AI tooling to empower creators while ensuring scalability and security. The phased rollout mitigates risk, and Kubernetes-based design simplifies global expansion.

Approvals:

• CTO: ___________________
• AI Lead: ___________________

Character Count: 3,872