PRODUCT — IN DEVELOPMENT
Mavenn.ai
AI Consensus Through Multi-Model Debate. One model can be confidently wrong. Multiple models in disagreement tell you something important.
THE INSIGHT
Confidence ≠ correctness
Mavenn is in active development. PhishHook.ai is already using a version of the consensus engine in beta. The full Mavenn platform — with configurable model selection, consensus tuning, and API access — is being built now.
If you have a use case that needs multi-model consensus before the platform launches, reach out. We run it on a consulting basis.
HOW IT WORKS
The consensus pipeline
Your query is sent to multiple AI models simultaneously — not sequentially. Each model processes independently, without influence from the others.
Each model returns its response. No averaging, no blending at this stage — raw outputs from each model, preserved.
Mavenn’s synthesis layer identifies points of agreement across model outputs. Where models converge, confidence is high.
Where models diverge, Mavenn flags the disagreement explicitly — showing you what’s contested, not hiding it in a false consensus.
Final output: consensus answer, confidence indicators, and a dissent summary. You see the agreement and the uncertainty.
HIGH-STAKES DECISIONS
When consensus matters
Any question where a single model being wrong has real consequences. Medical information, legal analysis, financial decisions.
Cross-referencing claims across models. If one model hallucinates, consensus exposes it.
Synthesizing complex research questions where multiple perspectives increase coverage and reduce blind spots.
PhishHook.ai uses Mavenn’s consensus to evaluate suspicious emails — multiple models vote, consensus protects.
EARLY ACCESS