Para empresas que querem crescer com controle

Um sistema para operar sua empresa com IA.

Vendas, marketing, atendimento, operação, financeiro e inteligência trabalhando sobre o mesmo contexto — para sua empresa ganhar escala sem perder controle.

Sua empresa inteira. Uma operação. IA trabalhando.

Ver a Centriu funcionando

Para empresas de qualquer setor que querem crescer com estrutura, controle e inteligência.

Representação ilustrativa do trabalho em execução

O problema

Sua empresa não deveria operar em pedaços

Não é falta de ferramenta. É que cada ferramenta resolve um pedaço, e ninguém é dono do todo. O custo disso não aparece numa fatura — aparece no mês seguinte.

Sistemas que não se falam
O mesmo dado é digitado de novo — e cada cópia conta uma história diferente.
Dados duplicados
Ninguém sabe qual versão é a verdadeira na hora de decidir.
Conversas perdidas
O cliente repete o contexto a cada canal e a cada pessoa.
Tarefas espalhadas
O trabalho vive em planilhas, mensagens e na memória de alguém.
Decisões sem contexto
A escolha é feita com o recorte que estava à mão, não com o que importava.
Automações desconectadas
Cada ferramenta automatiza um pedaço e ninguém enxerga o todo.

Nenhum desses problemas se resolve comprando mais um software. Eles se resolvem quando a operação inteira passa a trabalhar sobre o mesmo contexto.

A resposta

Tudo trabalha sobre o mesmo contexto.

A Centriu não é uma coleção de produtos. É uma plataforma em que clientes, áreas e agentes leem e escrevem no mesmo lugar.

Cliente 360

Um contexto, não vários cadastros

Cada conversa, tarefa, campanha, cobrança e decisão fica ligada ao cliente e à operação — não a um sistema isolado.

Clientes
Comercial
Atendimento
Operação
Financeiro
Marketing
Inteligência
Agentes
Consultoria

As áreas abaixo são partes da mesma operação, não produtos separados. Elas compartilham conta, permissões e histórico.

IA que trabalha — não um chatbot ao lado

A inteligência artificial da Centriu participa da operação: entende contexto, prepara trabalho, executa rotinas e pede aprovação quando importa.

Entende o contexto

Lê a operação, o cliente e o histórico antes de propor qualquer coisa.

Prepara o trabalho

Redige, organiza, calcula e deixa pronto para revisão humana.

Executa rotinas

Roda o que é repetitivo dentro dos limites definidos pela empresa.

Recomenda a próxima ação

Cruza áreas, identifica prioridade e mostra o motivo da recomendação.

Pede aprovação

O que é sensível passa por uma pessoa antes de valer. Sem exceção silenciosa.

Mantém histórico e aprende

Registra o que aconteceu e usa o resultado para melhorar a próxima decisão.

O que a Centriu faz

Uma plataforma, várias frentes de trabalho. Tudo no mesmo contexto, com a IA participando do que dá para acelerar.

Comercial

CRM, pipeline, oportunidades, propostas, follow-up e automações.

Atendimento

WhatsApp, Instagram, Messenger, chat do site e e-mail no mesmo contexto.

Operação

Tarefas, responsáveis, prazos, aprovações, agenda e capacidade.

Financeiro

Contas, cobranças, caixa, fiscal, margem, IR, orçamento e inteligência econômica.

Marketing

Planejar, produzir, aprovar, publicar e medir conteúdo e campanhas.

Inteligência

Painéis, alertas, previsões, recomendações e leitura executiva.

Agentes

Rotinas executadas continuamente pela IA com controle e aprovação.

Consultoria

Diagnóstico, estratégia, transformação, acompanhamento e especialistas.

Para quem é

Para empresas que querem crescer — e crescer certo.

A Centriu faz sentido quando o volume aumenta e a estrutura precisa acompanhar. Não é sobre setor nem tamanho: é sobre a operação conseguir sustentar o próximo estágio.

As vendas crescem, mas o processo não acompanha.

As áreas trabalham separadas e o cliente sente.

A operação depende de pessoas específicas.

O financeiro ficou complexo e o caixa perde visibilidade.

O atendimento cresce em volume e em canais.

A IA entra sem contexto, controle ou histórico.

Se a empresa ainda não começou a crescer, a Centriu também ajuda a preparar a estrutura para o primeiro salto.

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Consultoria

Tecnologia sozinha não resolve uma operação mal desenhada.

A Centriu combina tecnologia, inteligência, método e acompanhamento. A consultoria diagnostica, desenha, implanta e acompanha — com evidências, não com apresentação.

Diagnóstico e imersão na operação

Estratégia e prioridades

Roadmap de transformação

Desenho da arquitetura operacional

Implantação conduzida

Acompanhamento com evidências

Benchmarking e leitura de mercado

Especialistas, rede e inteligência coletiva

A participação de especialistas depende da solução e do contrato. Não é um item incluído automaticamente em toda contratação, e não prometemos acompanhamento humano ilimitado.

Veja a Centriu funcionando na sua operação

Uma conversa para entender onde a sua operação perde tempo, dinheiro ou capacidade — e o que a Centriu faria primeiro.

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problema

AI Policy Decision and Conflict Resolution Automation: When Two Policies Disagree, the Answer Is Never a Coin Toss

Two governance policies that both apply to the same AI action and disagree about the outcome is not a rare edge case. It is the normal state of any organization with more than one team writing rules. Centriu TrustOps resolves that disagreement through a fixed, seven-step order. It starts with a platform-wide mandatory floor that no team's policy can weaken. Then it goes through explicit denial over permission, mandatory over optional, specific over generic, configured priority, the most restrictive outcome, and finally a stable tiebreak. This way, the same inputs always produce the same decision. The decision names exactly which principle settled it. The same production engine can be run in simulation. This includes retroactively against real historical events, without ever writing to the actual decision log. An audit trail that can be rewritten is not an audit trail.

Quero resolver isso

Why "just write a policy" stops working the moment a second policy exists

A single governance policy is easy to understand. It says what it says, and it applies. The moment a second policy exists — a department's own rule alongside a platform-wide one, a project-specific exception alongside a general standard — the question stops being "what does the policy say" and becomes "which policy wins, and why." Answering that inconsistently, or worse, letting whichever policy happens to be checked first win by accident, is how a platform-wide safety floor quietly gets overridden by a team that wrote a more specific-sounding rule with no idea it was competing with anything.

A single governance policy is easy to understand. It says what it says, and it applies. The moment a second policy exists — a department's own rule alongside a platform-wide one, a project-specific exception alongside a general standard — the question stops being "what does the policy say" and becomes "which policy wins, and why." Answering that inconsistently, or worse, letting whichever policy happens to be checked first win by accident, is how a platform-wide safety floor quietly gets overridden by a team that wrote a more specific-sounding rule with no idea it was competing with anything.

Quero resolver isso

How Centriu TrustOps decides and resolves policy conflicts

The evaluation engine is a clear function. Given the same input, the same context, and the same policy versions, it always produces the same result. It reads no external clock, touches no database, makes no network call, and does not depend on generative AI at any point in the decision itself. Generative AI may explain a decision after it has been made. But the engine's own design statement is clear that it "should not be the final authority." A decision resolves into one of twelve real outcomes. These range from a plain allow to allowed-with-restrictions, allowed-with-masking, requiring one or multiple approvals, requiring additional information, blocked, prohibited, suspended by risk, or escalated to an incident or a review. It is never just a bare yes or no. When more than one policy applies to the same action, precedence is resolved through a fixed seven-step order. First, a platform-wide control marked as a mandatory minimum always wins. This is to ensure no organization's "more specific" local policy can weaken a security floor that was never meant to be adjustable. Second, an explicit denial beats a permission. Third, a mandatory policy beats an optional one. Fourth, a more specific scope beats a more generic one. Fifth, a policy's own configured priority number applies. Sixth, if policies still tie, the more restrictive outcome wins. Seventh, a stable identifier comparison breaks any remaining tie. This guarantees the same result every time, regardless of the order records happen to be returned in. Every resolved conflict names, clearly, which of these seven steps actually decided it. It is never left as an unexplained outcome. The identical engine also powers simulation. Testing a candidate policy runs through the exact same evaluation logic used in production. It is not a separate test copy that could quietly diverge over time. A retroactive simulation can be run against real historical events to answer "what would this policy have done" without ever overwriting the original decisions those events actually produced. The simulated result is written to its own separate record. An audit trail that can be rewritten after the fact is not an audit trail at all.

Ver como funciona

What is actually built today

A clear, deterministic evaluation engine. It has no clock, database, or network access inside the decision logic. There is no generative AI making the call. It only explains it afterward if asked.
Twelve real decision outcomes. These range from a plain allow to masking, single or multiple approval requirements, additional-information requests, blocking, prohibition, risk-based suspension, and escalation to an incident or a review.
A fixed, seven-step conflict-resolution order. It starts with a mandatory platform floor first. Then it goes through explicit denial over permission, mandatory over optional, specific over generic, configured priority, most-restrictive-wins, and a stable tiebreak. This is applied identically every time.
Every resolved conflict names the specific principle that decided it. It does not present an unexplained final outcome.
A mandatory-minimum-control flag that a more specific, locally-scoped policy can never override. This closes off the obvious way local specificity could otherwise weaken a platform-wide security floor.
A policy simulator that runs the identical production evaluation engine. It is not a separate test copy. This way, a simulated result is never at risk of quietly diverging from what production would actually do.
Retroactive simulation against real historical events. It writes its result to its own separate record and never modifies the original decision log.
An explicit, conservative translation function. This connects this general-purpose policy engine's twelve outcomes to the narrower five-outcome vocabulary the AI request gateway itself uses. It does not rely on the two systems' terms coincidentally lining up.

Conhecer os recursos

What changes operationally

The structural change is that a policy conflict stops being resolved by accident. It is no longer by whichever rule happened to be checked first. It becomes a documented, repeatable outcome that names the exact principle that decided it. A platform-wide security floor cannot be quietly weakened by a locally-scoped policy that merely looks more specific. A policy change can be tested against real historical traffic before it ever governs a real action. The test uses the exact logic that will actually run in production. Centriu attaches no figure to what this prevents. It depends entirely on how many overlapping policies an organization's AI governance program maintains.

The structural change is that a policy conflict stops being resolved by accident. It is no longer by whichever rule happened to be checked first. It becomes a documented, repeatable outcome that names the exact principle that decided it. A platform-wide security floor cannot be quietly weakened by a locally-scoped policy that merely looks more specific. A policy change can be tested against real historical traffic before it ever governs a real action. The test uses the exact logic that will actually run in production. Centriu attaches no figure to what this prevents. It depends entirely on how many overlapping policies an organization's AI governance program maintains.

Quero esses resultados

A department policy meets a platform floor (illustrative scenario, not a real client)

A department writes its own policy allowing a specific AI action under certain conditions. This is scoped narrowly to that department's own project. It seems more specific than the general organization-wide rule. Separately, a platform-wide policy marked as a mandatory minimum control prohibits that same action outright for a category of sensitive data. This is regardless of scope.

When an action matching both policies is evaluated, specificity does not win here. The platform floor is checked first. This is because it is flagged as a mandatory minimum, and it prevails regardless of how narrowly the department's policy was scoped. The decision record names exactly which of the seven precedence principles resolved it: the mandatory-floor rule, not the specificity rule that would otherwise have applied.

Before rolling out a proposed change to the department's own policy the following month, the team runs it through simulation against the last quarter's real request history. The retroactive run shows exactly which past actions would have been treated differently. It does this without altering a single original decision record. This gives the team real evidence of the policy's effect before anyone is actually governed by it.

A department policy meets a platform floor (illustrative scenario, not a real client)

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How to start

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Frequently asked questions

Does AI decide the outcome of a policy evaluation?

No. The evaluation engine is a clear, deterministic function with no AI in the decision path. Generative AI can explain a decision afterward but is never the final authority.

What happens when two policies apply to the same action and disagree?

A fixed seven-step precedence order resolves it. It starts with a mandatory platform-wide floor. Then it goes through explicit denial, mandatory-over-optional, specificity, configured priority, the most restrictive outcome, and a stable tiebreak. The decision names which step resolved it.

Can a department's own, more specific policy override a platform-wide security floor?

No. A policy flagged as a mandatory minimum control is checked before specificity is considered at all. This is to prevent that.

Does simulating a policy affect real decisions?

No. Both live and retroactive simulation write to their own separate records. They use the identical production engine. The original decision log is never modified.

Can a policy be tested against real past events before it goes live?

Yes. Retroactive simulation runs a candidate policy against real historical events. It shows what it would have done without altering any original decision.

How many possible decision outcomes exist?

Twelve outcomes exist. These range from a plain allow to masking, one or multiple approval requirements, additional-information requests, blocking, prohibition, risk-based suspension, and escalation to an incident or review.

What does Centriu TrustOps cost?

It is sold by subscription with a published starting price. Exact current values are on the central pricing page.

Ainda tenho dúvida

See how Centriu TrustOps resolves policy conflicts

Two governance policies that both apply to the same AI action and disagree about the outcome is not a rare edge case. It is the normal state of any organization with more than one team writing rules. Centriu TrustOps resolves that disagreement through a fixed, seven-step precedence order. It starts with a platform-wide mandatory floor that no team's policy can weaken. Then it goes through explicit denial over permission, mandatory over optional, specific over generic, configured priority, the most restrictive outcome, and finally a stable tiebreak. This way, the same inputs always produce the same decision. The decision names exactly which principle settled it. The same production engine can be run in simulation. This includes retroactively against real historical events, without ever writing to the actual decision log. An audit trail that can be rewritten is not an audit trail.

Quero resolver isso