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What are our Goals?

We are building the System Level Intelligence for Mixture-of-Models (MoM), bringing Collective Intelligence into LLM systems.

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What are our Goals?

We are building the System Level Intelligence for Mixture-of-Models (MoM), bringing Collective Intelligence into LLM systems.

Core Questions​

Our project addresses five fundamental challenges in LLM systems:

1. How to capture the missing signals?​

In traditional LLM routing, we only look at the user's query text. But there's so much more information we're missing:

  • Context signals: What domain is this query about? (math, code, creative writing?)
  • Quality signals: Does this query need fact-checking? Is the user giving feedback?
  • User signals: What are the user's preferences? What's their satisfaction level?

Our solution: A comprehensive signal extraction system that captures 9 types of request signals from requests, responses, and context.

2. How to combine the signals?​

Having multiple signals is great, but how do we use them together to make better decisions?

  • Should we route to the math model if we detect both math keywords and math domain?
  • Should we enable fact-checking if we detect either a factual question or a sensitive domain?

Our solution: A flexible decision engine with AND/OR operators that lets you combine signals in powerful ways.

3. How to collaborate more efficiently?​

Different models are good at different things. How do we make them work together as a team?

  • Route math questions to specialized math models
  • Route creative writing to models with better creativity
  • Route code questions to models trained on code
  • Use smaller models for simple tasks, larger models for complex ones

Our solution: Intelligent routing that matches queries to the best model based on multiple signals, not just simple rules.

4. How to secure the system?​

LLM systems face unique security challenges:

  • Jailbreak attacks: Adversarial prompts trying to bypass safety guardrails
  • PII leaks: Accidentally exposing sensitive personal information
  • Hallucinations: Models generating false or misleading information

Our solution: A plugin chain architecture with multiple security layers (jailbreak detection, PII filtering, hallucination detection).

5. How to collect valuable signals?​

The system should learn and improve over time:

  • Track which signals lead to better routing decisions
  • Collect user feedback to improve signal detection
  • Build a self-learning system that gets smarter with use

Our solution: Comprehensive observability and feedback collection that feeds back into the signal extraction and decision engine.

The Vision​

We envision a future where:

  • LLM systems are intelligent at the system level, not just at the model level
  • Multiple models collaborate seamlessly, each contributing their strengths
  • Security is built-in, not bolted on
  • Systems learn and improve from every interaction
  • Collective intelligence emerges from the combination of signals, decisions, and feedback

Why This Matters​

For Developers​

  • Build more capable LLM applications with less effort
  • Leverage multiple models without complex orchestration
  • Get built-in security and compliance

For Organizations​

  • Reduce costs by routing to appropriate models
  • Improve quality through specialized model selection
  • Meet compliance requirements with built-in PII and security controls

For Users​

  • Get better, more accurate responses
  • Experience faster response times through caching
  • Benefit from improved safety and privacy

Next Steps​

Learn more about the core concepts: