ELI LLM routing system - Enterprise-Ready AI Orchestration - www.aikitech.ai
(Ongoing)·Completed
What problem was solved
Tested the AiKiTech LLM Router platform to verify intelligent routing between multiple Large Language Models using developer-provided API keys. The project involved functional API validation, routing analysis, concurrent load testing, performance measurement, latency analysis, cost comparison, and developer experience evaluation.
Note: Projects: Functional API Testing | AI Routing Validation | Concurrent Performance Testing | Developer Experience Testing. Skills: REST API, JSON, Authentication, Error Handling, API Documentation Validation, Prompt Engineering, AI Routing Logic, AI Model Comparison, Routing Decision Analysis, Cost Optimization, Load Testing, Performance Testing, Python Automation, Multithreading, CSV Reporting, Performance Metrics, SDK Validation, Multi-language Code Examples, API Usability Testing, Technical Documentation Review, Developer Experience Feedback.
Key Features
- Features Tested
- API Key Management
- Application Profile Configuration
- LLM Router API
- Cross-provider routing
- Concurrent request handling
- Performance Dashboard
- Playground
- Routing metadata
- Cost reporting
- Latency metrics
- Caching behavior
- Developer documentation
Project Outcome
Successfully validated API authentication, application profile configuration, intelligent model routing, and response generation across multiple LLM providers. Conducted concurrent load testing, analyzed routing decisions using returned metadata, measured latency and cost metrics, and identified opportunities to improve developer documentation, error transparency, and routing diagnostics through structured feedback.
Challenges
Testing required differentiating platform behavior from upstream LLM provider limitations, including rate limits and model availability. Verifying routing consistency involved designing prompts with varying complexity and analyzing routing metadata, latency, cost metrics, and selected models to determine whether routing decisions aligned with expected behavior.
Project Media
Team members
Technologies Used
Technical Architecture
Configured third-party LLM API keys (OpenAI, Google Gemini, Anthropic) within the AiKiTech platform and created Application Profiles that generated AiKiTech-specific API keys. Client applications authenticated using the AiKiTech API key, which routed requests through the AI Router to the appropriate upstream language model based on prompt analysis. Responses returned routing metadata, latency metrics, token usage, cost information, and model selection details, enabling validation of routing behavior and performance.
Project Integrity
All source code and architectural documentation for this project are maintained under version control. Technical walkthroughs are available upon formal request.