Perplexity built the right architecture for multi-model AI: dispatch three frontier models in parallel and compare their outputs. What the product does not have is governance over the synthesizer that combines those outputs before any human reads them. This case study maps the gap, proposes four published open-source governance components as the overlay, and identifies why Perplexity’s own engineering culture already practices the checkpoint pattern the synthesizer needs.
Perplexity
AI Personalization Prompts
by Platform Deploy HAIA-RECCLIN governance to your AI tools. Each platform stores custom instructions differently. Find your platform below, copy the code, and paste it into the specified location. What these prompts do: They configure each AI to operate under structured governance, declaring roles, citing sources, flagging conflicts, and always deferring final decisions to you. […]
The Case for AI Provider Plurality in Evidence-Based Research
ChatGPT refused to Align Family Structure, Perplexity researched Biological Front-Loading and Economic Compounding and Claude confirmed it. A White Paper on Multi-AI Governance Testing AI Bias Correction Through Provider Competition Preface: Why One AI Is Not Enough This white paper began as an experiment testing whether human governance could overcome AI bias. It ended as […]