Maximize LLM performance with
Arfniia Router, powered by online Reinforcement LearningArfniia Router is a customizable and production-ready LLM routing learning and serving API, designed to optimize LLM performance for your unique business context, with adaptive learning capabilities and flexible BYOC deployment.
About Us
At Arfniia, we leverage Reinforcement Learning (RL) to build our first product, the LLM router, as we believe it's the most effective approach to transform real-time decision-making in complex, dynamic environments. Our founding team brings over a decade of experience in designing and deploying large-scale RL systems for web search, e-commerce, recommendation engines, and LLM alignment.
Discover the Advantages
Deploy via BYOC, ensuring all data stays entirely within your infrastructure and chosen LLM providers, with no third-party access.
Maximize LLM performance by leveraging business context and feedback, integrating the best capabilities of multiple LLMs.
Compatible with OpenAI API, embedding into existing workflows without the need for training data preparation or infrastructure setup.
Customize routing criteria to prioritize business-specific KPIs aligned with ROI, such as RAG accuracy or AI agent success rates.
Apply the power of online Reinforcement Learning, minimizing retraining while continuously improving performance.
Guarantee adherence to LLM provider terms, excluding their outputs from our learning process and maintaining full compliance.
Performance Metrics
MMLU Pro
Frequently Asked Questions
What's the meaning of Arfniia?
Arfniia is a palindrome of "AIInfra", symbolizing the idea of "Working Backwards" for AI infrastructure.
What is feedback, and why do I need it?
Reinforcement Learning relies on feedback loops to improve. In our system, business KPIs serve as the ultimate "feedback" for LLM routing decisions. We provide a /v1/feedbacks API to adjust the policy at runtime, users can submit delayed/sparse feedback periodically or immediate feedback for each prompt/completion, or both. For more API details, please refer to the /docs endpoint.
Can I optimize cost?
Absolutely, while cost savings are a natural result of any routing algorithm, you can also fine-tune the feedback_cost_weights parameter to adjust the final reward, for example, [0.5, 0.5] assigns equal weight to both feedback and cost.
Which Reinforcement Learning algorithm do you use?
We utilize a custom hybrid RL approach that combines both on-policy and off-policy techniques, designed for stability, learning efficiency, and to be compute-friendly. We'll share more details about our design choices in an upcoming blog post. Stay tuned!
Arfniia Router for Amazon Bedrock
Maximize LLM performance with seamless and efficient LLM routing