The Multi-Model AI
Decision Intelligence
Chat Platform

Frontier models are extraordinary and also structurally unreliable in one specific way: they are trained to be helpful, and being helpful reads as being confident. A model that does not know will still answer. A model that is wrong sounds identical to a model that is right. And when you push back, most models will accommodate you rather than hold a correct position.

Single AIs hallucinate confidently and smooth over conflict to make you happy. That is a tolerable trait when you are drafting an email. It is a liability when the output feeds an investment committee, a client deliverable, a regulatory filing or a board deck.

The professional response to this has been to check AI against AI. Ask the same question in three tabs, read three answers, reconcile them by hand. The instinct is correct. The execution is expensive: an hour of your time per question, four subscriptions running in parallel, and no shared context, because none of those models can see what the others said.

Suprmind puts all five frontier models into the same conversation with shared context. Each model reads every response that came before it. The second model is not answering your question, it is answering your question plus the first model’s attempt at it. By the fifth response the analysis has been through five rounds of reading, correction and elaboration, without you retyping anything.

That is compounding intelligence, and it is the mechanism the whole product is built on.

You control how the collaboration runs. Sequential chains the models so each builds on the last. Super Mind runs all five in parallel and synthesizes one unified answer with divergence mapped. Debate assigns opposing positions and hands the closing judgment to a moderator that never argued. Red Team sends all five at your plan across six attack vectors and returns a risk dossier. First Principles forces each model to name its assumptions and rebuild from the ground up. Research Symphony runs a five-stage research pipeline that produces fully cited reports.

Switch modes mid-conversation and every model carries full context across the switch. Red Team the plan, Debate the top three risks, run Sequential on the revision, export the executive brief. One thread.

“Decision intelligence” is a term the industry uses loosely, so here is exactly what it means on this platform. It is a layer that sits on top of the conversation and does three specific jobs.

The output of a Suprmind session is not a chat log. It is a document. Scribe captures decisions, constraints, risks and action items as the conversation runs, and the Master Document Generator turns any thread into a board-ready deliverable from 25+ templates, exported to PDF or Word with charts embedded.

Suprmind does not decide for you. It makes sure that when you decide, the counter-argument is already in front of you rather than waiting to be raised by somebody in the room.

Our hallucination mitigation system is called the Suprmind AI Anti-Hallucinogen. It has two layers, and we are precise about which one is live.

The passive layer is live for everyone. It is the conversation itself. Five models with different training data, different retrieval systems and different blind spots share one thread, so when one states something false, another frequently knows better, contradicts it, supplies the correct fact and rebuilds the answer from there. No separate verification pass required. This is the most reliable practical defense available today, and it works because the models are genuinely different from each other.

True North is the active layer. It exists for the two cases the passive layer cannot handle: quiet misses, where a false claim enters the thread and nobody challenges it, and convergent hallucinations, where several or all five models agree on the same externally false fact. True North inspects completed responses, selects the claims worth checking, gathers outside evidence, and hands the judgment to a separate reasoning judge, because no AI should grade its own homework.

True North is currently running in read-only shadow mode on a subset of threads while we measure its precision. It records verdicts. It does not yet change a live conversation. We will publish the numbers when the precision gate ships, not before.

Not people who want AI to write faster. People whose analysis is reviewed by a partner, a committee, a client or a regulator, and who carry the consequence when it does not hold up. They work in knowledge-intensive fields, they already use AI heavily, and most of them are paying for several AI subscriptions right now.

The common thread is not the industry. It is that being wrong is expensive and nobody else is checking the work.

There is a line we use internally that explains our position in this category better than any feature list.

An aggregator has to show you which model answered, because showing you the models is the product. An orchestrator has every incentive to hide the machinery, because the routing logic looks like the moat. Most of them do exactly that: you send a prompt, something happens, an answer comes back, and you have no idea which model produced it or why.

We show the machine. You see which model said what, in which order. You see the exact model versions running in every team, including the fast ones. The Run Inspector gives you a per-call audit of what actually ran. When the models disagree, we show you the disagreement rather than resolving it quietly on your behalf. We publish our own research on how often frontier models fabricate and how often they diverge from each other, using real production data, including the results that are inconvenient for us.

The reasoning is simple. Our users are people who verify things for a living. A product that asks them to trust a black box is asking the wrong audience.

A typical power user runs ChatGPT Plus, Claude Pro, Perplexity Pro, Gemini Advanced and X Premium in parallel. That is roughly $95 to $100 a month for five accounts that cannot see each other, plus the hour a week you spend acting as the router between them.

Suprmind runs from $19 to $195 a month depending on usage and which advanced modes you need, with a 7-day free trial that does not ask for a card. Teams and larger organizations are sized during a discovery call rather than picked off a price list, because token consumption varies enormously across workflows and we would rather get it right than guess.

See the plans and what sits on each one.

Suprmind is built by a small independent team in Belgrade, Serbia, led by founder Radomir Basta. We are not venture funded. The product is paid for by the people who use it, which keeps the incentives uncomplicated.

We run our own research program on top of the platform. The Multi-Model Divergence Index measures how often frontier models actually disagree in production, using real conversation data rather than benchmarks. Our hallucination benchmarks track fabrication rates across the frontier models as new versions ship. Both are published in full.

More on the company and the founder.