Multi-Agent Anonymous Decision Framework
Research project building an ensemble AI system that aggregates responses from multiple LLMs (Claude, Gemini, Perplexity) with anonymization pipeline to eliminate model bias. Evaluates outputs for production-grade reliability in critical decision-making scenarios.
In Planning - Coming Soon
Project Goal
Create a framework for unbiased, reliable AI decision-making by aggregating multiple LLM outputs through anonymization and consensus mechanisms.
Project Highlights
Performance Metrics
90%
performance
95%
accessibility
90%
seo
Key Features
- ▹Ensemble AI System: Aggregates 3+ LLM providers for comprehensive decision analysis.
- ▹Bias Elimination: Anonymization pipeline removes model-specific patterns and branding.
- ▹Production-Grade Evaluation: Automated scoring system for output reliability and consistency.
- ▹Domain-Specific Weighting: Dynamic model weighting based on topic expertise.
Technology Deep Dive
Multi-LLM Orchestration
Aggregates responses from Claude, Gemini, and Perplexity with weighted scoring based on domain expertise.
Anonymization Pipeline
Strips identifying markers from LLM outputs to eliminate brand bias in evaluation.
Consensus Engine
Statistical analysis of agreement patterns across models for confidence scoring.