Straight answers about 3Dogs Nexus.

3Dogs Nexus is a structured decision-intelligence platform — an “AI council.” Instead of one chatbot handing you a single confident answer, it runs your high-stakes decision past many independent AI models, makes them debate it adversarially, grounds them in live cited research, and returns one documented, calibrated recommendation with the assumptions, risks, and dissent laid out. Its motto: “We don't make your decisions. We make them better.”

Those are single models: fast, fluent, and confident — even when they're wrong. 3Dogs isn't a better model; it's a process laid over many models. It clarifies the real question, gathers current evidence, forces a panel to argue and challenge assumptions, preserves the disagreement, and hands you a recommendation you can defend — not just a paragraph you have to take on faith. As we put it: the model isn't the moat; the process is the product.

Conceptually, no — multi-agent debate and consensus scoring are an established way to improve AI reliability. Executionally, yes. Three things set 3Dogs apart: grounding over pure logic (a Discovery phase forces live cited research so the panel debates facts, not hallucinations), falsifiability and calibration (every output carries a probability, a resolution date, and gets Brier-scored against reality), and a single calm interface (“Ask Rex”) instead of a noisy wall of agents.

It's the sharpest question about the whole approach — and if you're not careful, yes: models that share training blind spots can raise consensus faster than truth. 3Dogs is designed to break that:

• Heterogeneous roster — models from different providers (Claude, GPT, Nova, Mistral, Llama, Qwen and more) trained on different data, so their errors don't line up.
• Assigned dissent — seats get genuinely opposing roles (devil's advocate, a “destroyer” tasked with breaking the argument), so the panel argues rather than agrees.
• Live grounding — Discovery anchors the debate to cited evidence, not shared priors.
• Preserved dissent — the minority report is printed, never buried.
• Calibration — confidence is Brier-scored against real outcomes, so confident agreement can't hide from being wrong.

People who make high-stakes calls without a board or a strategy team behind them: small-business owners betting real money, consultants and advisors who want a disciplined second opinion before they present, and leadership teams that need a documented, auditable basis for a decision. It's not for casual tasks like drafting emails or summarizing articles — it's for the decision you can't easily take back.

Anything consequential and defensible: hire or wait, expand or hold, acquire or build, enter a market, change the model, invest or preserve cash — plus public-policy and planning questions and time-pressured crisis calls. Same process, very different stakes.

A rotating roster of 40+ models across providers — including Claude (Opus 4.8, Sonnet), GPT-5.5, Nova Pro, Mistral Large, Llama 4, Qwen3, Kimi K2, GLM-5, Nemotron, DeepSeek and others. The mix is deliberate: different training data means decorrelated blind spots. The specific models can change; the process stays the same.

Minutes for a fast case; longer for the deep method on the highest-stakes calls, where the panel runs multiple rounds. Either way you watch it happen live — you can literally see the models research and debate — and you get a written brief at the end.

A plain-English brief with a single clear recommendation, the assumptions it rests on, the risks that matter most, a confidence level, what would change the call, and metrics to watch — plus the preserved dissent from any analyst who disagreed. Written for a busy decision-maker, not a data scientist.

It means we don't just sound confident — we track whether we were right. Forecasts carry a probability and a resolution date, and we grade them against actual outcomes using a Brier score. Over time that separates the analysts who are genuinely accurate from the ones that merely sound smart. Calibration over confidence.

No, we don't train on your data. Your cases are used to produce your analysis, not to train models, and are retained only briefly (a short retention window) under our published Terms and Privacy Policy. See Terms and Privacy.

Solo $99/mo (owner-operators), Professional $299/mo (consultants & advisors), Business $999/mo (up to 5 users on one business domain), and Enterprise from $20,000/yr (custom, prepaid annual or a dedicated deployment). You can start free — no card, no confirmation email, instant access. The cost of one bad decision is usually far greater than the cost of a second opinion.

Open a web-connected AI (ChatGPT, Gemini, Claude, or Perplexity) and ask it to explain 3dogs.ai in plain English — then ask it why 3Dogs might fail. You'll get an independent read from a model that has no reason to flatter us. That's the whole idea: one confident AI is a trap, so let your AI weigh in on ours.