I kept asking ChatGPT important questions and getting confident, articulate answers. Then I'd ask Claude the same question and get a completely different take. Sometimes they'd agree. Sometimes they'd contradict each other entirely.
That's when it hit me: every AI has blind spots.
ChatGPT is trained on different data than Claude. Claude reasons differently from Gemini. Kimi K2 and DeepSeek were trained with different values than Western models. When they agree, you can be more confident. When they disagree, you've found something worth investigating.
The insight isn't in any single answer—it's in the pattern of agreement and disagreement.
For decisions that matter—architecture choices, strategic analysis, risk assessment—you want more than one expert opinion. AI should be no different.
Every model sounds certain. That confidence can be misleading. When multiple models reach the same conclusion independently, you have actual signal.
When models clash, they're surfacing trade-offs you might have missed. The argument is the insight.
Consensus Studio shows you when models agree (green), when they're still refining (yellow), and when they fundamentally disagree (orange). You see the confidence, not just the answer.
During deliberation, you don't know which model said what—they're just "Agent A", "Agent B", "Agent C". This eliminates brand bias. You can't favor GPT if you don't know it's GPT. When you're ready, reveal identities and get a full audit trail. Like jury deliberation, peer review, or blind wine tastings—the methodology that makes evaluation rigorous.
I wanted a tool that would:
I couldn't find it. So I built it.
"This isn't about replacing your judgment. It's about giving you better information before you decide."
Single-model AI is like asking one advisor. Multi-model AI is like convening a panel. Both have their place. But for high-stakes decisions, I'll take the panel.
— Pete
Consensus Studio is a native macOS app. Your data stays local. You bring your own API keys. The models deliberate. You decide.