LLM Matrix System Description

Cogniforce LLM Matrix

Our LLM Matrix is part of the Model Governance layer. It’s designed to intelligently manage and control how user requests are routed among different underlying large language models (LLMs), such as OpenAI’s GPT, Anthropic’s Claude, Google’s Gemini, or Mistral.

How the LLM Matrix Operates:

1. Understand the request: It parses the user’s intent—whether it’s drafting, summarizing, analyzing, or extracting—and assesses sensitivity and compliance needs.

2. Apply policies: It checks for jurisdictional rules, data handling policies (e.g., GDPR, EU‑only processing, zero‑retention), and business constraints.

3. Select the best model: Based on trade-offs like quality, latency, cost, risk, and policy alignment, it chooses the optimal model for the job.

4. Execute with safeguards: Prompt sanitization, guardrails, moderation, zero‑data‑retention agreements, and compliance measures are enforced.

5. Explain the decision: The system tags the response with metadata—indicating which model was used, the policy applied, tokens used, latency, etc.—and logs this for audit purposes.

Why This Matters:

• Performance consistency: By routing each task to the most suitable model, the LLM Matrix helps ensure quality outputs with predictable performance.

• Regulatory alignment: It supports compliance by enabling region‑specific processing and data‑governance controls.

• Transparency & auditability: Detailed logs and metadata empower audits, troubleshooting, and compliance checks.

• Cost and risk management: You can enforce quotas, fallback logic, provider whitelists, and more, helping control spending and reduce operational headaches.

Summary Table