Five Frontier AIs, Six Modes, One Decision Layer
Suprmind runs GPT, Claude, Gemini, Grok and Perplexity inside one shared conversation. Each model reads what came before and builds on it. Then a decision intelligence layer scores where they disagreed, settles it, and hands you a document.
This page is the complete feature reference. Every mode, every control, every memory system, and what each one is actually for. If you want the shorter story first, read what Suprmind is and why it works this way.
See Five Frontier AI Models Working in One Shared Conversation
What multi-AI orchestration actually means
Most people use AI one model at a time. That is the single-perspective trap. A model can be excellent overall and still invent a number, miss the assumption the whole answer rests on, or contradict itself two paragraphs apart without noticing. You have no second reader.
Multi-AI orchestration means several frontier models take part in the same conversation, the platform controls how they take part (order, roles, synthesis), and every model sees the full context before it answers.
In Sequential mode, Claude does not just see your question. It sees your question plus what GPT already said. Gemini sees your question, GPT’s response and Claude’s correction. That is compounding intelligence. Every response is built on everything before it, which is why the fifth answer is not a fifth version of the first.
Decision intelligence is the layer on top. Suprmind scores where the models disagreed, settles a specific contradiction into a structured decision brief, and can run a full validation pipeline before you commit to anything. Orchestration produces the conversation. Decision intelligence turns it into something you can defend in a room full of people who will push back.
Five frontier AIs.
Different strengths. Shared context.
Every provider trains on different data and fails in different ways. Suprmind uses those differences instead of treating models as interchangeable.
GPT
Structured reasoning and technical precision. Strong at breaking a messy problem into ordered parts.
Claude
Nuanced analysis and close reading. Catches edge cases, hidden assumptions and ethical exposure.
Gemini
The largest context windows in the lineup. Long-document synthesis, multimodal input, Google Search grounding.
Grok
Fast reasoning with live web and X access. Direct, and willing to tell the rest of the room it is wrong.
Perplexity
Real-time research with citations. Grounds the thread in current, checkable sources.
Every model in the boardroom is the full reasoning variant from its provider. Exact versions rotate as providers ship, and new releases reach your teams within days. The current roster for every plan lives on the pricing page, and you can swap which model fills any seat yourself in Settings.
AI Teams: the right models
for the question you actually asked
Running the smartest model in the world on a formatting request is a waste. Running a fast model on a merger decision is a risk. Suprmind groups models into named teams, one seat per provider, and picks between them for you.
The A-team
Your smartest models. Deep deliberation on high-stakes, novel or strategic problems where being wrong is expensive.
Reasoning depth is highest here by default.
Operators
The realistic default for roughly 85% of work. Strong and well rounded, without running maximum brainpower at maximum cost on every turn.
Daily Drivers
Speed and large context. Quick lookups, document parsing, structured extraction, data work, first-pass research.
Smart Selector
A separate AI reads the whole thread plus the message you are about to send, then routes it to the best-fit team the moment you hit send. Quality goes where it matters. Spend does not run away from you.
Pick a team manually and it applies to that turn only, then hands back to Auto. Turn on Full Control and your pick stays pinned for the rest of the session. The per-turn reset is deliberate. It stops the A-team quietly staying on and burning a month of usage in a week.
Usage without token math
The Usage Control tab shows a plain-language runway: roughly how many days of work you have left at your current pace, plus a simple note when you are on track. No token counters, no dollar amounts, no maths homework.
Near your monthly usage limit, Suprmind does not stop you mid-thought. Around 80% you get a heads-up. Around 90% it moves you to Daily Drivers so you keep working, tells you exactly what changed, and offers a one-click Usage Booster to bring the heavier teams back. No hard walls.
Custom rosters, always. Every provider exposes a dropdown of all its models, so you can put any model in any team and run all-flagship, all-fast, or any mix you want. Defaults apply until you override them. On Spark, where the Smart Selector is not included, you steer with @mentions instead.
Different problems.
Different orchestrations.
A mode is a thinking pattern, not a setting. It decides how the five models work together on your question. You can switch modes mid-conversation and every model carries full context across the switch.
Sequential
A → B → C → D → E
Each AI responds in turn and reads everything before it. The default mode and the deepest. Each model gets a position-aware system prompt, so the first knows it is setting the foundation and the last knows it is closing. Response order is yours to set in Settings.
Best for: complex analysis, research synthesis, technical architecture, iterative building.
All plans
Super Mind
(A + B + C + D + E) → Synthesis
All five respond at once. A dedicated synthesis engine reads every output and streams back one unified answer: themes extracted, consensus mapped, divergence flagged, outlier insights kept. Four synthesis strategies, from the default Synthesis to Consensus-only and Adversarial.
Best for: quick multi-perspective reads, fact verification, time-sensitive calls.
All plans
Debate
Openings → Rebuttals → Moderator
Structured phases across three turns. Opening statements, then direct rebuttals, then a dedicated moderator that never debated writes the verdict. A Bridge-Builder persona finds real common ground, and a minority opinion is never suppressed for losing 4 to 1.
Best for: strategy validation, thesis stress-testing, controversial calls.
Pro and up
Red Team
Six vectors → Risk dossier
Five AIs attack your idea across financial, technical, reputational, regulatory, operational and edge-case vectors, then run a mitigation pass. Output is an exportable risk dossier with a kill chain that shows how small flaws cascade into total failure.
Best for: pre-launch validation, due diligence, pre-mortems, security review.
Pro and up
First Principles
Assumptions → Axioms → Rebuild
Each model names its assumptions, strips the question down to fundamental truths, then rebuilds the analysis from the ground up rather than reasoning by analogy to something that worked once for somebody else.
Best for: novel problems, contrarian analysis, decisions where convention is suspect.
Pro and up
Research Symphony
Retrieve → Analyze → Check → Challenge → Synthesize
A five-stage pipeline with a specialized model sandboxed into each role, running 15 to 30 minutes in the background on dedicated infrastructure. Produces 10,000+ word fully cited reports. Push notification when it lands.
Best for: market research, competitive analysis, literature reviews, technical due diligence.
Enterprise
Mode chaining is the workflow that pays back fastest. Red Team your launch plan to find the risks, Debate the top three, Sequential the resulting strategy, then generate the executive brief. One thread, one context, one document at the end.
Direct control over who answers
Tag specific models to control exactly who responds and in what order. The models you did not tag stay informed but silent. Available in every mode on every plan, which makes it the way Spark users steer their teams.
| Pattern | Example | What happens |
|---|---|---|
| Single model | @claude review this contract | Only Claude responds. Everyone else reads it. |
| Ordered chain | @perplexity @gpt @claude | They answer in the order you tagged them, each building on the last. |
| Selective team | @grok @claude @gemini | Those three answer the same prompt. The other two skip the turn. |
| Parallel tasks | @grok check sentiment @perplexity find competitors @claude analyze both |
Each model executes its own assignment while seeing the full context. |
@Mentions is an orchestration method, not a seventh mode. It works inside Sequential, Super Mind, Debate, Red Team and First Principles alike.
Disagreement is the feature.
Most AI tools are tuned to hand you one smooth, confident answer. Suprmind does the opposite.
When you ask a single AI a question, you get its best guess. You have no way to know whether that answer would survive a model trained on different data, with different reasoning habits and different blind spots.
Suprmind surfaces disagreement on purpose. When Claude says X and Grok says Y, that is not a bug. That is information. Weak ideas get exposed when they cannot withstand four other readers. Strong ideas get stronger when they survive five models building on each other.
When five models converge, your confidence is earned rather than assumed.
When they disagree, you have located the exact assumption, tradeoff or missing fact that needs your attention.
That is the whole point.
Three layers that turn a conversation
into an auditable decision
Five AIs disagreeing is interesting. Five AIs disagreeing with the contradiction scored, settled and documented is usable. This layer is what separates Suprmind from every tool that just gives you access to more models.
DCI
Scores how much the AIs agreed and disagreed across the conversation. Divergence appears as an inline card directly under the message bubbles the moment models split, plus a sidebar tab with per-turn and session totals. The topics that generated the most debate get flagged as the ones worth investigating.
DCI surfaces disagreement automatically. You do not have to go looking.
Pro and up
Adjudicator
A sidebar tab you invoke when a specific disagreement matters. It analyzes where the AIs split, reads the Scribe notes and the full conversation, and produces a structured decision brief: context analysis, a recommendation, and a confidence assessment. It streams as it builds.
DCI surfaces the split. The Adjudicator settles it, and gives you documentation for why you decided what you decided.
Pro and up
DVE
A six-stage pipeline for decisions you cannot walk back. Investment go or no-go, launch readiness, strategic pivots, vendor selection, major procurement.
The output is an FMEA-style risk register and a full decision dossier with executive summary, minority opinions and action items. Decisions that cannot withstand adversarial scrutiny should not be made.
Pro and up
Inside the Decision Validation Engine
1
Intake
A three-step wizard captures the decision statement, options, success criteria, constraints, risk tolerance and timeframe.
2
Clarify
Super Mind extracts a validation manifest. Ambiguities parsed, unstated assumptions identified, missing information flagged.
3
Red Team
Adversarial attack across all six vectors. Output is an FMEA-style risk register scored by severity, likelihood and detectability.
4
Debate
Structured argumentation on the identified risks. Output is a contention map of what is genuinely contested.
5
Synthesis
Final call: GO, NO-GO or GO WITH CONDITIONS, with the reasoning attached to each risk that drove it.
6
Doc Gen
An auto-generated decision dossier. Executive summary, full analysis, minority opinions, action items and owners.
The risk register scores every risk on severity, likelihood and detectability, then ranks by Risk Priority Number so the list arrives sorted by what to handle first. Run it at suprmind.ai/validation.
You make the call. Suprmind makes sure you make it knowing what the disagreement actually was.
Five AIs keep each other honest.
True North checks what all five might miss.
The Suprmind AI Anti-Hallucinogen is a dual-layer AI hallucination mitigation system. The first layer is the conversation itself. The second layer works outside it, because no AI should grade its own homework.
The passive layer
One AI hallucinates and hopes you do not notice. In Suprmind, four other models are reading that answer in the same thread. One of them frequently knows better, says so, replaces the bad claim and continues the reasoning from the corrected position.
That correction happens inside the ordinary conversation. It costs nothing extra and it needs no verification pass. It works because the models have different training data, different cutoffs, different retrieval systems and different blind spots, and because Suprmind’s prompts reward correcting the room rather than agreeing with it politely.
What it is not is proof. A later model can wrongly challenge a correct answer. A confident first answer can anchor the four that follow. Agreement can reflect shared training rather than independent verification. Which is exactly why the second layer exists.
Live, all plans
True North
True North runs continuously alongside the conversation, inspecting completed responses at response boundaries. It has no stake in the answer, because it is not one of the models that wrote it.
It exists for the two cases the passive layer cannot handle. Quiet misses, where a wrong claim enters the thread and nobody challenges it. And convergent hallucinations, where several models, or all five, agree on the same externally false fact because they share a training artifact or because one confident answer anchored the rest.
Its highest value is often where there is no disagreement at all. Five models nodding along is not evidence. Evidence is evidence.
Live
ANALYZE → RESEARCH → JUDGE
Three separate jobs, three separate systems. The researcher does not grade its own research, and the models that produced the original answer do not get a say in whether they were right.
ANALYZE
A reasoning model reads each completed response and decides which claims are checkable and worth checking. Numbers, dates, named entities, citations, and legal, financial and scientific statements. It does not blindly extract every sentence, because verifying opinions is theatre.
RESEARCH
A separate research system gathers current external evidence for the selected claim. This stage returns evidence only. It has no opinion about whether the claim is right, and it is not asked for one.
JUDGE
A reasoning judge compares the original claim against the gathered evidence and rules on it. Three outcomes, no hedging into a fourth.
| Verdict | What it means |
|---|---|
| SUPPORTED | The available evidence substantially supports the claim as stated. Not a permanent guarantee. Facts change and evidence can be incomplete. |
| CONTRADICTED | At least one load-bearing part of the claim is contradicted by the evidence. |
| UNVERIFIABLE | The system cannot responsibly rule. The evidence is insufficient, inaccessible, ambiguous, or the claim is not cleanly checkable. This does not mean probably false. |
What we do not claim
We do not promise zero hallucinations. We do not promise that five models will always catch one another. Any product promising you an AI that never invents anything is inventing something.
What Suprmind promises is a system built around the reality that hallucinations happen. The models correct many errors naturally as the conversation unfolds. True North independently investigates the claims they may miss or collectively get wrong. High-stakes claims get more than one path to correction.
One AI can be confidently wrong. Five AIs can correct one another. Evidence is there for the cases where all five are wrong together.
Next in line: confirmed corrections injected back into the running thread, and the working memory cleaned so a contradicted claim stops propagating into later turns. Read the hallucination benchmarks we publish rather than taking our word for any of this.
Context that survives the conversation
The usual failure mode in AI work is context loss. Re-explaining your project to a fresh window for the fourth time that day. Suprmind solves it at four levels: within a turn, within a thread, within a project, and across every project you own.
Context Fabric
The layer that keeps all five models synchronized. Every message, turn and tool output is logged in a central ledger, and the perfect context is rebuilt for each model on every turn: project instructions and memory first, then the master doc snapshot, then live Scribe entries, then recent turns in full, then this turn’s responses untruncated, then your question, which is never cut.
Older turns compress into rolling summaries so context stays sharp instead of bloating. In a 50-message conversation the models still remember message three. You never manage any of it.
Server-side conversation memory
Three of the five providers maintain native server-side memory of your conversation, so they reference their own earlier reasoning directly rather than reconstructing it from re-sent text. The result is a model that develops its thinking across a thread instead of reacting to the last message.
The remaining two use prompt caching, which keeps the stable parts of a long conversation cheap to re-read. Your usage is debited at the cached rate, not the raw one.
Document Intelligence Pipeline
Standard AI chat falls apart on long documents because every model has a different context limit. This pipeline turns uploaded files into a shared, queryable knowledge layer instead. Text extraction, chunking, embedding, sibling artifact extraction for the figures, tables and code blocks inside the file, then retrieval at query time.
Drop a 200-page PDF once. Every model answers from the exact same passages, with citations, so nobody quietly drifts from the source. Live status badges tell you when a file is indexing, ready or failed.
Project Knowledge Graph
As you talk, Suprmind passively extracts entities (people, companies, technologies, decisions, concepts) and the relationships between them (depends on, implements, replaces, relates to). The graph grows with every conversation in the project.
Ask what depends on the deployment architecture and you get connected answers rather than a keyword search. Semantic retrieval finds the entity, graph expansion pulls in its neighbours.
Cross-thread Project Memory
Threads are not islands. Decisions made in Monday’s chat about pricing are already known when Thursday’s chat starts on go-to-market. Decisions, stated preferences, established facts, constraints and your own terminology all persist.
The tenth conversation in a project is meaningfully smarter than the first. That is the compounding part.
Master Project
Projects are isolated by default so unrelated work does not bleed together. Master Project deliberately breaks that wall: query the knowledge graphs of every project at once, search files uploaded anywhere, reference a decision from another workstream. Results carry source-project attribution so you always know where an insight came from.
Save to Project turns any strong response or master document into permanent, searchable project knowledge. Move to Project folds a chat you started outside a project into one, history intact.
Every conversation makes the next one smarter.
This is the part that does not show up in a feature comparison and matters most by month three.
1
Converse
Five models answer, Scribe captures the decisions and risks as they land.
2
Save
Responses, master documents and files join the project knowledge base.
3
Index
Vector search and the knowledge graph structure all of it automatically.
4
Compound
The next conversation starts with everything the last one established.
In month one you are explaining context. By month six the boardroom knows your competitors, remembers why you chose one architecture over another, and recalls that your CFO wants conservative estimates on anything that reaches the board.
From conversation to finished document
Nobody hands their board a chat log. Suprmind closes the gap between the thinking and the artifact without a single copy-paste.
Scribe
A real-time note-taker running in the sidebar alongside the thread. It captures decisions, constraints, assumptions, risks, action items and insights as they happen, each entry tagged with how much the AIs agreed on that point.
Nothing to click and nothing to summarize afterwards. After a long thread you already have the structured version.
Auto-updating Master Doc
Every project has a master document that maintains itself in the background from Scribe notes. It always reflects the current state of decisions, constraints and progress.
You do not generate it. You open it and it is current. You never start from a blank page.
Master Document Generator
25+ built-in templates across research, business, technical, marketing and communication: Executive Brief, Research Paper, Competitive Analysis, SWOT, Decision Record, Pitch Document, Statement of Work, Case Study, White Paper, Meeting Notes, FAQ, Onboarding Doc and more.
Generate at any point in the conversation, not just the end, and generate more than one from the same thread. Pick which model writes it, because style matters: Claude for prose, GPT for technical rigour, Perplexity for citation-heavy work. Exports to PDF and DOCX with charts embedded inline. Write your own template once and reuse it forever.
Smart Visualizations
The AIs draw charts inline as they answer. Bar, line, heatmap and table, more than one per response, interactive in the thread with hover values, zoom and pan.
Download any chart as a PNG with a transparent background for slides. The Visuals tab collects every chart from the conversation, and also accepts pasted data if you just want a chart out of numbers from somewhere else. Charts embed automatically into PDF and DOCX exports.
Prompt Assistant
Paste a rough brain dump and get back a structured prompt the boardroom performs measurably better against. Ambiguity in a single-model chat costs you one bad answer. Ambiguity across five models compounds into five.
It also writes your project instructions and per-model personalities from a project description, and takes plain-English refinements after that.
Quick Tools
Sometimes you do not need a five-AI boardroom. You need to fix grammar, change tone, summarize, expand, build a table, or pull every email address out of a mess of text.
Thirteen instant local tools and nine AI-powered ones, each one or two seconds. Chain them together, undo any step. Live at suprmind.ai/tools.
How you steer the room
Suprmind stays out of your way until you want it to do something specific. Then it gets specific.
Deep Thinking
Makes every model reason harder before it answers. The thinking blocks are preserved, so you can audit how a conclusion was reached rather than only reading what it concluded. Worth it for multi-step logic and strategy. Skip it for lookups.
Response Length
Key Points, Balanced or Full Detail. New chats start on Key Points, switchable at any time. Full Detail is for the analysis you are going to turn into a document.
Custom Sequential order
Drag your models into the order that fits the work. Research-heavy task, put Perplexity first. Need deep analysis to lead, start with Claude. Saved for every future Sequential conversation.
Per-project AI personalities
Write separate system instructions for each of the five models inside a specific project. Claude on edge cases and ethics, GPT on structure, Gemini on scope, Grok kept terse. Most tools cannot do this at all.
Personalization profile
Describe your role and how you think once, in plain English. It gets injected into every model’s system prompt across every project and every mode. A CFO at a Series B startup stops having to say so.
Language matching
Write in Spanish, get five Spanish answers. Switch to Japanese mid-thread and the room follows. No language selector, no setting, works across all modes and all five models.
Stop and redirect
Interrupt any model mid-response, type your correction, send. The interrupted model answers your redirect first, then the rest follow. You never have to wait out a bad answer.
Message queuing
Send your next thought while the boardroom is still working on the last one. Queued messages process automatically, which means you can plan a whole research sequence in advance and walk away.
Voice in, voice out
Speech to text on the composer, a Listen button on every response, auto-continue across answers and a floating player. Hands-free thinking on the walk.
Streaming controls
Adjust render speed, toggle token streaming, control auto-scroll, jump back to the latest message. Small things that matter when you read along with five models at once.
Soft delete with recovery
Deleted a chat by accident? Every deleted session sits in a 30-day recovery window. Click restore. No archive folders, no support ticket.
Push notifications
Fire off a long run and get told on your phone when it lands. Useful for Research Symphony and for long Sequential turns you kicked off before a meeting.
Upload once. Every model reads the same thing.
PDF, DOCX, TXT, Markdown, CSV, JSON, XLSX and code files. Chunked into overlapping semantic passages, embedded, indexed, and retrieved at query time so answers are grounded in your material rather than the model’s memory of something similar.
| Plan | Projects | Files per project | Per file |
|---|---|---|---|
| Spark | 4 | 10 | 5 MB |
| Pro | 20 | 30 | 5 MB |
| Frontier | 50 | 60 | 9 MB |
| Power | Unlimited | 100 | 15 MB |
| Enterprise | Custom | 150 | Custom |
Attach a file and a banner shows per-model context utilization before you send, so you know in advance whether an upload will crowd out a model’s window. Conversation history is kept for 30 days on Spark and indefinitely on every paid plan.
You should be able to check the machine,
not just trust it
Every one of these exists because a professional deliverable has to survive somebody asking where a number came from.
Tool usage transparency
Coloured pills under every response show exactly which tools it used: web search, X, your project files, the knowledge graph, Google grounding. Click any pill to see the actual URLs, page titles, filenames and relevance scores. You are never guessing where information came from.
Fresh data tagging
When Perplexity or Grok pulls from live sources, their responses carry a fresh-data tag. Every model that answers after them sees that tag and treats current information differently from training-data knowledge. You always know whether a statistic came from a live source or a model’s memory.
Run Inspector
Every AI call is recorded with its system prompt, context, response, tools used and cost. Useful for debugging a surprising answer, validating a compliance workflow, or just satisfying curiosity about what actually ran. Available on every plan.
Identity affirmation
Every model gets an explicit identity statement in its system prompt, and other models’ responses are labelled clearly in context. Claude knows it is Claude. Subtle, and it matters when five models are reading each other all day.
Auto-recovery
If a response stalls mid-stream, the platform resumes the turn cleanly rather than failing. Per-provider retry on silent timeouts, honest error events for any provider that gets skipped. If one model is down, the other four carry on and you are told which one dropped.
No silent substitutions
We never quietly swap in a different model and let you assume you got the one you picked. If a provider is unavailable, the response says so.
EU and Swiss hosting
Application hosting in Germany, primary database in Zurich. Data residency by default rather than as an upgrade. Encryption in transit and at rest, project and user isolation, and your data is not used to train models.
Procurement paperwork
DPA, MSA, security questionnaire responses and the sub-processor list are available on request. FastSpring is the merchant of record, which handles VAT and sales tax across jurisdictions and simplifies the invoice side of procurement.
The whole boardroom on your phone
Suprmind is a Progressive Web App. Two taps to install on Android through the prompt, or on iOS through Share and Add to Home Screen. No app store, no separate download.
All five models, all six modes, file uploads, Scribe, master documents and voice. Full screen, offline-aware, push-notification ready, with touch-sized controls and swipeable prompt cards. Your projects sync across every device.
Built for the part where
procurement gets involved
Everything above, plus the infrastructure and paperwork a team needs to adopt it properly.
Bring your own keys
Use your own API keys for any or all five providers. Your billing, your rate limits, your data agreements. Per-provider toggles let you mix platform keys and your own. Keys are encrypted at rest, never exposed in logs, transmitted over TLS, revocable at any time. If a key fails, the platform falls back and logs that it did.
Dedicated provider workspaces
Suprmind sets up isolated workspaces with each provider for your account. Your queries and data are never pooled with other customers. No noisy-neighbour risk on rate limits and no red flag on a shared-account question in a security review.
Managed allocation, one invoice
One invoice covering all five providers instead of five bills, five procurement processes and five quotas. A per-seat platform fee billed annually with volume discounts, plus a managed AI allocation sized to your team’s actual workload.
Research Symphony
The five-stage research pipeline, on dedicated infrastructure. A single run is heavy enough that it would consume a large share of a self-serve monthly allowance, which is why it sits with managed allocations that are sized to absorb it.
Maximum-context models
The largest available context windows from every provider as standard, for full codebases, long document sets and decision packs that would break any single-AI tool.
Direct founder support
Escalate to the founder directly. No tier-one queue, no automated triage. A 99.5% uptime SLA with service credits and dedicated response times sits behind it.
Permissions that match how teams actually work
| Project level | What they can do |
|---|---|
| Read | View conversations and generate master documents. Cannot send messages. |
| Write | Full chat inside the project. Cannot change project instructions or settings. |
| Admin | Everything inside the project, including configuration. |
Team-level roles sit above that: Member, Admin and Owner, controlling who can invite, who can remove, and who manages the account itself. Read-only stakeholders are the quiet win here. Legal and finance can see the analysis and pull their own documents without ever entering the thread.
What we are building next
We would rather show you what is coming than pretend the product is finished.
Adjutant
A passive second brain. A project-aware strategist that tracks where a project actually stands, notices the thread you abandoned three weeks ago without concluding it, and recommends what to ask next.
Not an autonomous agent. It does not act on your behalf. It watches the project and tells you what you are missing, which is a different and more useful job.
The closed loop
Detecting a bad claim is half the job. The other half is stopping it from poisoning the rest of the thread.
Next: a confirmed contradiction gets injected back into the conversation, the earlier claim gets marked in the thread’s working state, and the Scribe and Context Fabric notes get cleaned so every model that enters later starts from the repaired baseline. The original claim and the verdict stay in the audit trail.
What people actually do with it
Every mode maps to a job. Here is how the modes get used in practice.
Strategic decision validation
Run a pivot, an investment or a senior hire through the Decision Validation Engine and walk out with a GO, NO-GO or conditional call backed by a risk register and a dossier. The version of the meeting where somebody asks what could go wrong has already happened.
Pre-mortem analysis
Red Team the launch plan before you ship it. Five models attack across six vectors and hand you the kill chain that shows how a small oversight becomes a failure. Then Debate the three risks that actually matter.
Deep market research
Perplexity grounds the thread in current sources, Grok adds what is happening this week, Gemini synthesizes across the long documents you uploaded, and the output leaves as a cited research paper rather than a scroll of chat.
Technical architecture review
Sequential mode with a custom order so the security read lands before the scalability read, and the cost read sees both. The knowledge graph remembers the decision six weeks later when somebody asks why.
Industry guides
Where the multi-model read changes the work itself.
AI for lawyers
Contract review, due diligence, legal analysis where a missed clause is the whole problem.
AI for medical research
Literature review and clinical synthesis with cross-model fact-checking on every claim.
AI for investment analysis
Deal evaluation and diligence where the thesis has to survive somebody hostile to it.
AI for Amazon listings
Listings that hit exact character limits without losing the argument.
AI for PPC copywriting
Exact-match ad copy for Google, Meta and LinkedIn, five drafts that argue about which one converts.
Everything else
The full how-to library, one workflow at a time.
Built for decisions that
cannot afford single-model thinking
Professional synthesizers
People who produce substantial deliverables by orchestrating AI conversations. Research reports, strategic analysis, technical documentation. Work where thoroughness beats typing speed.
Before Suprmind: running the same question through three tools, pasting the answers into a doc, and doing the synthesis by hand. Context lost between tabs. Hours on mechanics.
Strategic leaders
Executives who need several perspectives on a critical call and do not have time to consult five AI tools by hand. Board decks stress-tested before the meeting. Competitive analysis where different models surface different threats.
Before Suprmind: presenting a recommendation built on one model’s output, then getting blindsided by the question it never raised.
Researchers
Analysts who need broad coverage with genuinely diverse viewpoints. Literature reviews that cross-validate sources. Hypothesis testing where the models argue opposing readings of the same data.
Before Suprmind: knowing one model has training gaps, with no way to find out where.
Consultants
Professionals whose analysis has to survive client scrutiny. Recommendations built from multiple perspectives. Blind spots eliminated before the meeting, not during it.
Before Suprmind: shipping work built on one AI perspective, then scrambling when the client asks whether you considered X.
Single-AI chat vs. Suprmind
| Single-AI chat | Suprmind orchestration |
|---|---|
| One model, one perspective | Five models reading and answering each other |
| You hope you picked the right model | Smart Selector routes each message to the right team |
| Manual comparison across browser tabs | Shared context, automatic synthesis |
| No way to check the answer | Debate, Red Team and the Decision Validation Engine built in |
| Contradictions get smoothed over | DCI scores them and the Adjudicator settles them |
| A confident wrong answer stands | Four other readers, plus independent verification behind them |
| Context lost when you switch tools | One memory layer across all five models |
| Every chat starts from zero | Project memory and a knowledge graph that compound |
| You copy-paste the output into a document | 25+ document templates, PDF and DOCX, charts inline |
Worth naming the distinction: an aggregator gives you access to multiple models. Suprmind orchestrates collaboration between them. Different product category. The comparison hub has the head-to-head breakdowns.
Pricing overview
Four self-serve plans plus Enterprise. Start with a 7-day free trial on Spark. No credit card.
Spark
$19/mo
Pro
$45/mo
Frontier
$95/mo
Power
$195/mo
Enterprise
Custom
Spark runs four providers across two teams with Sequential and Super Mind. Pro opens the full five-model boardroom, Debate, Red Team, First Principles and the whole decision intelligence layer. Frontier adds Master Project across workspaces and priority everything. Power adds your own API keys and the highest self-serve capacity. Enterprise adds Research Symphony, team seats, dedicated provider workspaces and managed allocation.
5
Frontier AI models
6
Orchestration modes
25+
Document templates
3
Decision intelligence layers
Frequently asked questions
What models does Suprmind use?
Frontier models from OpenAI, Anthropic, Google, xAI and Perplexity. Pro and above run all five providers. Spark runs four. Versions rotate as providers ship, usually within days of release, so the exact roster lives on the pricing page rather than here. You can open any team and swap which model fills each seat.
Why not just use ChatGPT or Claude directly?
You can, and for plenty of work you should. What you do not get is a second reader. One model gives you its best guess with no signal about what it missed. Suprmind gives you four more readers on the same context, and tells you where they disagreed.
How is this different from five browser tabs?
Shared state. In tabs, Claude has no idea what GPT said. In Suprmind, Claude reads GPT’s answer before writing its own. Three practical differences: every model sees what the others said and can build on it or challenge it, context is shared so you never re-explain your project, and synthesis happens automatically in Super Mind.
Is Suprmind an AI aggregator?
No. An aggregator gives you access to multiple models, usually one at a time or side by side. Suprmind puts them in the same thread with shared context and controls how they interact. The models read each other. That is a different product category.
Does it hallucinate?
Individual models still can, and anyone claiming otherwise is selling you something. Suprmind runs a dual-layer AI hallucination mitigation system. The passive layer is the conversation itself, where a model that knows better challenges and corrects an earlier answer on the spot. True North is the active layer, which independently analyzes completed responses, researches the checkable claims, and rules SUPPORTED, CONTRADICTED or UNVERIFIABLE from outside the model chain. We publish our own hallucination benchmarks rather than asking you to take our word for it.
Do I have to manage which models run?
No. Models come grouped into preset teams and the Smart Selector routes each message to the right one automatically. Override any turn manually, or pin a team for the whole session with Full Control. Every team is fully editable in Settings if you want to build your own roster.
What happens when I hit my usage limit?
Nothing dramatic. Around 80% you get a heads-up. Around 90% Suprmind moves you to the Daily Drivers team so you keep working, tells you what changed, and offers a one-click Usage Booster to bring the heavier teams back. There are no hard walls and no mid-thought cutoffs.
Can I switch modes in the middle of a conversation?
Yes, and it is the workflow we would push you towards. Every model carries full context across the switch, so you can Red Team an idea, Debate the risks that surfaced, then run Sequential on the surviving plan without restating anything.
How does the context window work across providers?
The Context Fabric manages a rolling window of critical context and rebuilds it per model on every turn. Older turns compress into summaries, recent turns stay in full, and your current message is never truncated. The platform makes sure the most relevant material reaches every model rather than letting whichever one hits its ceiling first drop the thread.
What can I actually export?
Master documents in PDF and DOCX with charts embedded, individual charts as PNG, the full thread as Markdown or plain text, and the Red Team risk dossier and DVE decision dossier as structured documents. Anything worth keeping can also be saved into project knowledge so future conversations can use it.
Is this only for research?
No. Any decision that benefits from more than one perspective: business strategy, technical architecture, legal review, investment decisions, medical second reads, regulatory work, content. If it matters enough to get right, it matters enough to validate with more than one model.
Glossary
Multi-AI orchestration
Several frontier models collaborating inside one conversation, with the platform controlling order, roles and synthesis.
Compounding intelligence
Each response is built on every response before it, so quality climbs down the chain instead of repeating.
AI Teams
Preset model groups: The A-team, Operators and Daily Drivers. One seat per provider, all of them editable.
Smart Selector
The router that reads your thread and message, then sends it to the best-fit team automatically.
Full Control
Pins your chosen AI team for the rest of the session instead of resetting to Auto after each turn.
Usage Booster
A one-click top-up that brings the heavier teams back when you are close to your monthly usage limit.
Super Mind
All five models answer in parallel, then a synthesis engine merges them into one answer with divergence mapped.
DCI
Disagreement / Correction Index. Scores and surfaces where the models split, inline as it happens.
Adjudicator
Turns a specific disagreement into a structured decision brief with a recommendation and a confidence assessment.
DVE
Decision Validation Engine. A six-stage pipeline that stress-tests a decision and issues a GO, NO-GO or conditional call.
AI Anti-Hallucinogen
Suprmind’s dual-layer AI hallucination mitigation system: multi-model self-correction plus independent verification.
True North
The active layer inside the AI Anti-Hallucinogen. Analyzes completed responses, researches claims, and rules on them from outside the model chain.
Context Fabric
The memory layer that keeps one shared context across all five models and every provider boundary.
Scribe
Real-time note-taker capturing decisions, constraints, risks and action items as the conversation happens.
Master Document
A finished deliverable generated from a thread. 25+ templates, PDF and DOCX, charts embedded.
Mode chaining
Switching orchestration modes mid-conversation while every model keeps full context across the switch.
@Mentions
Directing a question to specific models. The rest stay informed but silent. A method, not a mode.
Master Project
A project that can query knowledge and files across every other project, with source attribution on the results.
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