LLM Integration for Game Engines
Architected AI-powered training system for government project, securing $2M in follow-on funding. Built production infrastructure and designed plugin architecture for seamless integration.
📋Overview
Led the architectural design and implementation of multiple AI-powered capabilities for Unity-based training applications. Created an underlying infrastructure that enables other developers to create their own services on top of a stable, foundational inferencing layer. The project required designing a scalable microservices architecture that could handle multiple concurrent LLM inference requests while maintaining low latency. Built using modern cloud-native patterns with Kubernetes orchestration, the system supports multiple LLM providers and can scale horizontally based on demand. The successful proof-of-concept demonstration to stakeholders resulted in $2M in follow-on funding, validating both the technical approach and business value. The system is now being deployed across multiple training programs.
🎯Challenges
- •Managing latency and cost of LLM API calls in real-time training scenarios
- •Designing a plugin architecture that integrated seamlessly with existing Unity workflows
- •Ensuring output quality and consistency from LLM-generated content
- •Scaling the system to handle multiple concurrent users and inference requests
- •Implementing proper prompt engineering and context management for domain-specific generation
- •Balancing between cloud costs and performance requirements
💡Solutions
- ✓Implemented intelligent caching and request batching to reduce API calls by 60%
- ✓Designed a modular plugin system using Unity's package manager with clear abstraction layers
- ✓Developed validation pipelines and output post-processing to ensure content quality
- ✓Built Kubernetes-based infrastructure with horizontal pod autoscaling and load balancing
- ✓Created domain-specific prompt templates and fine-tuned retrieval strategies using Langchain
- ✓Implemented hybrid approach with local models for low-latency inferencing
🚀Outcomes & Impact
- ✓$2M in secured follow-on funding
- ✓Architecture patterns enable extensibility, modularity and reusability
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