AI-powered decision intelligence

Many minds.
One verdict.

A council of AIs built to disagree — so you don't have to.

Ask your question. Watch a council of AI minds debate, challenge each other, and converge on the strongest possible answer — then share it.

Try one question — free See how it works

No signup required  ·  One free council run  ·  Shareable verdict

Live example

"What's the best go-to-market
strategy for a new SaaS?"

Four minds. Four roles. One question. Watch the council work.

A
Model Alpha
Builder · OpenAI

Launch with a narrow ICP — one industry, one pain point, one channel. Product Hunt for visibility, then double down on whatever converts. Get to 10 paying customers before optimizing anything. Speed beats strategy at zero revenue.

B
Model Beta
Strategist · Anthropic

The channel matters less than positioning. Most SaaS fails because it competes on features. Define the category you want to own, then find the 50 people who need it most. Your first customers are your brand. Choose them carefully.

C
Model Gamma
Critic · Gemini

Both miss the unit economics question. GTM without knowing your CAC:LTV ratio is gambling. Before any channel, validate that someone will pay. One cold email that converts beats a Product Hunt launch that doesn't. Charge from day one.

D
Model Delta
Challenger · DeepSeek

All three assume GTM is primarily a channel or positioning problem. The invisible premise: that you have a product worth marketing at all. None questioned whether the SaaS solves a problem people will pay for versus one they think they have. You could execute a perfect GTM and still fail because the product doesn't survive contact with a paying customer.

J
Judge · Synthesis
Final verdict

Agreement: All four converge on starting narrow — one ICP, one problem, validated demand before scaling. The Challenger's assumption attack holds: GTM only matters if the product survives a paying customer.

Final answer: Before any GTM strategy, get one stranger to pay. Then identify 10 target companies manually, send 50 personalized cold emails, and charge from the first conversation. Position around a category you can own, not features you can build.

Confidence High
Convergence 81 / 100
Rerun needed No

How the council
reaches a verdict

Convergence runs a structured deliberation — not a poll, not a summary. A real debate with roles, pressure-testing, and synthesis.

01

You ask one question

Any question worth thinking about carefully. Strategic decisions, business models, technical choices, creative direction.

02

A council of minds answers independently

Builder, Strategist, and Critic each respond to your question blind — no groupthink. Then the Challenger reads all three and attacks the assumption they all shared without realising it.

03

They debate each other

Round two: Builder, Strategist, and Critic see all four Round 1 responses and revise. The Challenger reads everything and finds what assumption survived the debate.

04

A judge synthesizes the verdict

The Judge reads all eight responses across both rounds, scores convergence, and delivers the strongest merged answer with a confidence rating.

05

You get a shareable result

Every verdict gets a permanent public URL. Share the council's reasoning with your team, your investors, or the world.

Why not just ask one AI?

Single models are overconfident. They rarely say "I'm not sure" or "here's where this breaks down." A Critic role forces that honesty.

Different models have different blind spots. OpenAI, Anthropic, Gemini, and DeepSeek are trained differently. What one misses, another catches — and the Challenger attacks what they all miss together.

Debate surfaces assumptions. The best insights in the council often come from one model challenging another's unstated premise.

Convergence is a signal. When three different models agree after disagreeing, you can trust the answer more than any single response.

You're building AI Council using AI Council. This product was designed, debated, and refined by the same system it describes.

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