vr framework case study

VR Training Framework Deep-Dive | Aman Sahu

VR Training Framework: 85% Faster Deployment

Siemens Energy | 2023-Present

⚠️ CONFIDENTIAL PROJECT: Due to NDA restrictions, detailed screenshots cannot be publicly displayed. High-level architecture diagrams included.

Project Overview

Built a data-driven VR training framework enabling non-technical trainers to create industrial training modules independently, reducing deployment time from 2 months to 10 days.

Impact: 85% faster deployment, eliminated developer bottlenecks, and enabled solo development across 3-4 major department projects.

The Challenge

Siemens Energy needed to rapidly deploy VR training modules for industrial equipment, but the traditional development process was creating massive bottlenecks.

Business Pain Points:
  • 2-month deployment cycle per training module.
  • Developer dependency: Every content change required C# programming.
  • Hardware-dependent testing: Weeks spent waiting for VR hardware availability.
  • No reusability: Each training was being built from scratch.

Technical Constraints:

  • Meta Quest 3 baseline (standalone VR, no PC tethering).
  • 90 FPS requirement for 8-hour industrial shifts to prevent motion sickness.
  • Offline-first: Factory environments have restricted/unreliable networks.
  • Multi-platform support: HoloLens 2, Quest 2/3, and future XR devices.

The Solution: Data-Driven No-Code Framework

Architected a JSON-driven VR training system with a custom visual editor, enabling trainers to create complex scenarios through a drag-and-drop interface without writing a single line of code.

Core Innovation: Parallel Action Engine

Traditional VR training uses linear steps. Our approach treats each step as a container of parallel actions:

{
  "actions": [
    {"type": "highlight", "targetId": "wrench", "blocking": false},
    {"type": "play_audio", "audioId": "instruction", "blocking": false},
    {"type": "wait_for_grab", "targetId": "wrench", "blocking": true}
  ]
}

Result: Multiple visual cues run simultaneously; non-blocking actions (audio/hints) run in the background; the container advances only when ALL blocking actions are complete.

Architecture Decisions

Decision #1: Hexagonal Architecture (Pure C# Domain Layer)

┌─────────────────────────────────────────────────────────┐ │ LAYER 1: Data Contracts (POCOs) │ └─────────────────────────────────────────────────────────┘ ↓ ┌─────────────────────────────────────────────────────────┐ │ LAYER 2: Behavioral Contracts (Interfaces) │ └─────────────────────────────────────────────────────────┘ ↓ ┌─────────────────────────────────────────────────────────┐ │ LAYER 3: Domain Logic (Pure C#, Zero Unity) │ └─────────────────────────────────────────────────────────┘ ↓ ┌─────────────────────────────────────────────────────────┐ │ LAYER 4: Unity Adapters (Engine Bindings) │ └─────────────────────────────────────────────────────────┘
  • Architecture designed for testability: Runs headless in NUnit in seconds.
  • Engine-agnostic: Portable to Unreal, WebXR, or future engines.
  • Trade-off: More files required (Interface + Implementation for each action).

Decision #2: Offline-First Two-Scene Architecture

Scene-1 (Online Gateway): Downloads content via cloud storage, verifies SHA-256 hashes, and preloads assets.
Scene-2 (Offline Runtime): 100% offline execution; zero network failures; zero JSON parsing during active runtime.

  • Zero training interruptions due to network issues on the factory floor.
  • 100% offline compliance: Passed strict Siemens security audits.

Decision #3: Event-Driven Zero-Allocation Execution

public class ActionContainer {
    private int _blockingCount;
    private int _completedCount;
    public event Action OnAllBlockingComplete; // O(1), zero allocation
    
    public void NotifyComplete() {
        _completedCount++;
        if (_completedCount == _blockingCount) {
            OnAllBlockingComplete?.Invoke();
        }
    }
}
  • 90 FPS sustained on Meta Quest 3.
  • Memory Efficiency: Reduced GC pressure through strict zero-allocation patterns.

Phase 2: AI-Powered Analytics Dashboard (In Progress)

Extending framework with server-side intelligence for training program optimization.

Server-Side AI Layer

(AWS Lambda + SageMaker)

  • Telemetry processing: Analyze complex training events.
  • Predictive insights: Identify struggling trainees early.
  • Content recommendations: Suggest behavior-based improvements.
Manager Dashboard

(React + AWS Amplify)

  • Real-time metrics: Completion rates and session duration.
  • Trainee performance: Track certification and progress status.
  • ROI calculator: Quantify prevented downtime costs.

Technical Implementation Highlights

// Domain Layer (Pure C#, Zero Unity Dependencies)
public class HighlightBehavior : IActionModule {
    private readonly IVisualTarget _target;
    private float _elapsedMs;
    
    public void Execute() {
        _target.SetHighlight(_data.colorHex);
        _elapsedMs = 0;
    }
}

// Unity Adapter Layer (Engine Bindings)
public class UnityVisualTarget : IVisualTarget {
    public void SetHighlight(string colorHex) {
        // Implementation using Renderer and Material APIs
        _renderer.material.SetColor("_EmissionColor", parsedColor);
    }
}
Why This Matters: Domain logic has zero Unity dependencies. It runs in headless unit tests (NUnit) in seconds and is portable to any engine (Unreal, WebXR) by simply writing new adapters.

The Results

Metric Before After Improvement
Deployment Time 2 Months 10 Days 85% Faster
Developer Dependency Required Optional Solo Development
Architecture Monolithic Hexagonal Testable & Portable
Framework Usage N/A 3-4 Projects Reusable Asset
"The architecture enabled us to move from code-heavy development to a content-first approach. We've scaled our VR training capabilities without increasing developer headcount."

— Technical Lead, Siemens Energy

Tech Stack

Unity 2022 LTS | C# .NET Standard 2.1 | Meta Quest 3 | Azure Cloud Services | NUnit | Hexagonal Architecture | Event-Driven Design

Key Learnings

  • Architecture Investment: Initial discipline cost paid off through significantly faster iterations and fewer production bugs.
  • Testability = Confidence: Decoupling logic allowed for rapid deployments and fearless refactoring of core systems.
  • DDD Portability: Domain logic proved reusable across platforms; a WebGL prototype was built in just 3 days using the same core logic.
  • Offline-First: Designing for the "worst-case" network is mandatory for factory floor success.

Want Similar Results?

I specialize in delivering high-authority architectural solutions for:

  • Industrial training systems with VR/AR.
  • Data-driven frameworks for non-technical users.
  • High-performance VR applications (90 FPS requirements).
  • Offline-first architectures for unreliable networks.
  • Testable Unity systems with domain-driven design.

Confidential project demos available during interview via screen share.

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