Making AI Disagreement Visible
MingLLM compares multiple frontier models in parallel, detects disagreement, and uses listwise arbitration to recommend a stronger answer with confidence signals.
Problem
Single‑model answers are opaque
Most AI systems provide one answer without exposing disagreement, uncertainty, or alternatives.
Reliability is uncalibrated
Users and enterprises have no visibility into whether the response is consistent across top models.
Solution: MingLLM Arbitration Layer
Multi‑model querying
Parallel calls to leading LLMs.
Disagreement detection
Measure divergence across responses.
Ranking + arbitration
Listwise ranking and winner recommendation.
Confidence‑aware output
Expose reliability signals to users.
Why This Is Different
Not another model
MingLLM sits above models as a judge.
Not prompt engineering
Reliability is computed, not prompted.
Not majority voting
Listwise ranking, safety‑aware scoring.
Technical Moat
Listwise learning‑to‑rank
Trained to rank candidate quality across heterogeneous model outputs.
Selective classification
Rejects low‑confidence outputs and flags ambiguity.
Safety‑aware scoring
Integrates risk analysis into arbitration.
Streaming / prefix arbitration
Early ranking before full generation completes.
Traction
Vision
We believe every AI answer should ship with reliability signals. MingLLM is building the trust layer for AI systems — from consumer apps to enterprise copilots.
Reliability scoring, auditability, and arbitration across the model stack.
Roadmap
Now
Arbitration layer for multi‑model responses with judge explanations.
Next
Enterprise reliability dashboards, SLAs, and audit trails.
Later
Standardized trust signals for AI across products and APIs.
Press Kit
Press materials and brand assets will appear here once the slide deck is added.
Contact Investor Relations
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