RAG Optimization Simulator
Show how ML Mind turns noisy retrieval into trusted context.
Many RAG systems send every retrieved chunk to the model. ML Mind filters for relevance, freshness, trust and citation value before inference.
Retrieved context
Before: retrieve broadly, send everything, pay for noise.
After: select trusted chunks, protect facts, reduce context safely.
Before / after
Before model input
After ML Mind selection
Token reduction
Context trust score
| ML Mind signal | Purpose |
|---|---|
| Relevance | Remove semantic noise |
| Freshness | Prefer current sources |
| Trust metadata | Protect citations and facts |
| Protected facts | Preserve numbers, dates and policies |
Turn this insight into a savings audit
Use your simulator result as the starting point for a free ML Mind AI FinOps audit.
Turn this page into action
ML Mind is designed to move from content to evidence: simulate your workload, generate a savings report, then request a structured AI FinOps audit.
1. SimulateEstimate waste across tokens, RAG, retries and GPU.
2. ValidateMap the estimate to your real telemetry.
3. ControlDeploy the safest control layer first.