Introducing ModelCouncil: Multiple AI Advisors, One Decision Cockpit

Today we're launching ModelCouncil.

The short version: Query Claude, GPT, Gemini, and Grok simultaneously against your project context. Get a synthesized Decision Board showing where they agree, where they disagree, and what you might be missing.

The longer version: Keep reading.

The Problem We Solved

If you're an executive or founder using AI for important decisions, you've probably developed a ritual:

Ask ChatGPT
Copy the response somewhere
Ask Claude the same question
Compare manually
Wonder what Gemini would say
Give up and go with your gut

You do this because you don't fully trust any single model. And you're right not to. Each model has blind spots, biases, and different training data. The answer you get depends on which model you asked.

But manually comparing models is tedious. And you lose context every time you switch tools.

ModelCouncil fixes both problems.

How It Works

1. Create a Project

Upload your context once: strategy docs, competitive analysis, financial models, whatever informs your decisions. This becomes your project's persistent context.

2. Ask Your Question

"Should we launch in Q2 or Q3 given our runway?" "Which of these three positioning options is strongest?" "What are the risks of this acquisition target?"

3. Get a Decision Board

We query up to four models in parallel (Claude, GPT, Gemini, and Grok) all seeing your same project context.

Then we synthesize their responses into a structured Decision Board:

Recommendation: The direction the models converge on
Key Reasons: Why this direction makes sense
Risks: What could go wrong
Validate First: What to check before committing
Consensus: Where all models agree
Disagreements: Where they diverge (and why it matters)
Rare Finds: Unique insights only one model surfaced

You see the full responses too. But the synthesis saves you from reading walls of text to find the signal.

4. Build on Previous Decisions

Here's what makes ModelCouncil different from just running multiple chat windows:

Your context compounds.

Every Decision Board gets compressed and stored. When you ask your next question, we retrieve relevant past decisions automatically. Your 50th query has the same quality context as your first. No degradation, no "context rot."

We call these Decision Threads. Related decisions chain together. You can branch to explore alternatives. Cross-reference insights from other threads when relevant.

It's not chat. It's a decision layer.

Diamond Mode

For your highest-stakes decisions, turn on Diamond Mode.

In Diamond Mode, models review each other's responses before the final synthesis, resulting in deeper analysis. The goal is to stress-test your thinking before you commit.

Diamond Mode costs more (more model calls), but for decisions that matter, it's worth it.

What It Costs

Platform: $149/month

Unlimited projects
Decision Threads with persistent context
Decision Board synthesis
Standard and Diamond modes

Usage: Pay for what you use

Credits cover API costs (we pass through model pricing with a small markup)
Typical decision: $0.30–$0.80 depending on context size
Buy credits in $25 / $50 / $100 increments
Credits never expire

14-day free trial. Full platform access, you just purchase credits to query.

We don't hide costs. Every Decision Board shows exactly how many tokens you used and what it cost.

Who This Is For

ModelCouncil is built for people who:

Already use AI for important decisions (and don't fully trust any single model)
Have recurring decision contexts (not one-off questions)
Value their time more than $149/month
Want a system, not just a chat window

If you're asking ChatGPT what movie to watch, this isn't for you.

If you're deciding whether to acquire a company, enter a new market, or restructure your team, and you want multiple perspectives without the manual overhead, this is what we built.

Why We Built This

I kept finding myself with the same workflow: ask Claude, ask GPT, manually compare, lose context, start over.

The multi-model insight isn't novel. Andrej Karpathy open-sourced a similar concept (LLM Council) around the same time we started building. The difference is what you do with the multiple perspectives.

His project scores answers for research. Ours synthesizes them into decisions for business, with context that compounds over time instead of degrading.

MIT researchers recently documented that even GPT-5 suffers from "context rot," the quality degradation that happens as conversations get longer. We built ModelCouncil specifically to solve that problem. Your 50th decision gets the same quality context as your first.

Start Your Trial

ModelCouncil is live now.

Create your first project. Upload your context. Ask a real question.

See what you've been missing by only asking one model.

Start your 14-day free trial →