Entende o contexto
Lê a operação, o cliente e o histórico antes de propor qualquer coisa.
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.
Para empresas de qualquer setor que querem crescer com estrutura, controle e inteligência.
Painel ilustrativo do Sistema Centriu com sete frentes trabalhando: atendimento, comercial, operação, agentes, inteligência, conteúdo e marketing. Cada frente mostra o trabalho concluído ou o próximo passo.
Representação ilustrativa do trabalho em execução
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 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.
As áreas abaixo são partes da mesma operação, não produtos separados. Elas compartilham conta, permissões e histórico.
A inteligência artificial da Centriu participa da operação: entende contexto, prepara trabalho, executa rotinas e pede aprovação quando importa.
Lê a operação, o cliente e o histórico antes de propor qualquer coisa.
Redige, organiza, calcula e deixa pronto para revisão humana.
Roda o que é repetitivo dentro dos limites definidos pela empresa.
Cruza áreas, identifica prioridade e mostra o motivo da recomendação.
O que é sensível passa por uma pessoa antes de valer. Sem exceção silenciosa.
Registra o que aconteceu e usa o resultado para melhorar a próxima decisão.
Uma plataforma, várias frentes de trabalho. Tudo no mesmo contexto, com a IA participando do que dá para acelerar.
CRM, pipeline, oportunidades, propostas, follow-up e automações.
WhatsApp, Instagram, Messenger, chat do site e e-mail no mesmo contexto.
Tarefas, responsáveis, prazos, aprovações, agenda e capacidade.
Contas, cobranças, caixa, fiscal, margem, IR, orçamento e inteligência econômica.
Planejar, produzir, aprovar, publicar e medir conteúdo e campanhas.
Painéis, alertas, previsões, recomendações e leitura executiva.
Rotinas executadas continuamente pela IA com controle e aprovação.
Diagnóstico, estratégia, transformação, acompanhamento e especialistas.
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.
Ver a Centriu funcionandoA 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
Uma conversa para entender onde a sua operação perde tempo, dinheiro ou capacidade — e o que a Centriu faria primeiro.
problema
Cross-module pattern memory, in Centriu Run, reads real events from six operational surfaces — overdue and in-progress tasks, mind map structure and which of its nodes actually became tasks, Decision Engine history, Action Center's prepared actions and saved meetings, plus direct custom events — and runs each stream through a specific, hard-coded rule. It does not use a generic anomaly detector. Seven distinct pattern types can surface this way: a client whose approvals go overdue at least twice, a team area absorbing six or more tasks with at least two overdue, a mind map with real structure but almost nothing turned into a task, a funnel with acquisition activity and zero conversion activity, decisions with a track record of actually getting executed, a prepared-action type repeating at least twice, and an executive question theme coming up at least twice in the Copiloto. Nothing here is guessed by a model — every trigger is a fixed number in the code. It is checked against real data pulled live each time.

The fact that a specific client's approvals are always late, or that one team keeps drowning while nobody quite names the trend, usually lives only in the head of whoever has been paying attention long enough to connect the dots. That person forgets, changes roles, or simply isn't in the room when the pattern would actually change a decision. Recognizing that something is recurring, rather than a one-off, is exactly the kind of judgment an operating system should be able to hold onto. It should not rely on someone's memory to carry it forward.
The fact that a specific client's approvals are always late, or that one team keeps drowning while nobody quite names the trend, usually lives only in the head of whoever has been paying attention long enough to connect the dots. That person forgets, changes roles, or simply isn't in the room when the pattern would actually change a decision. Recognizing that something is recurring, rather than a one-off, is exactly the kind of judgment an operating system should be able to hold onto. It should not rely on someone's memory to carry it forward.
Every time the Memory Engine runs, it rebuilds its event stream live from six real places in the product. It doesn't read from a separate "events" table someone has to populate by hand. Overdue and in-progress tasks come straight from the task list itself. It uses the same overdue check the rest of Run relies on elsewhere. Mind map structure comes from each organization's saved maps. It includes which of a map's nodes have actually been linked to a real task. Decision Engine contributes the history of what got suggested, approved, deferred or executed. Action Center contributes prepared actions by type and saved meeting records. A sixth stream — direct custom events — is written straight to a shared, per-organization table. This way, a pattern one person's actions create is visible to a colleague, not stuck in that one person's browser.
Seven pattern rules run against those streams. Each has a threshold written directly into the code rather than tuned by a model: a client needs at least two overdue tasks whose stage or name reads as an approval step before "recurring approval delay" fires for that client. An operational area needs at least six total tasks and at least two overdue before "area overload" fires. A mind map needs at least four nodes with no more than a fifth of them actually linked to a task before "strategy without execution" fires. A map needs at least one node that reads as acquisition-related and zero that read as conversion-related before the funnel-imbalance pattern fires. At least two approved-or-executed decisions are needed before a decision-adherence pattern is recorded. At least two prepared actions of the same type are needed before that type is flagged as recurring. Finally, at least two Copiloto questions on the same inferred theme are needed before that theme is flagged. None of these numbers move based on how interesting the result looks — they are the same for every organization.
Six real event streams feeding one engine: overdue/active tasks, mind map structure and task-linkage, Decision Engine history, Action Center's prepared actions, saved meeting records, and direct custom events shared per organization.
Seven distinct, separately-triggered pattern types, each with its own fixed numeric threshold coded directly rather than tuned by a model.
Recurring client approval delay: fires once a specific client accumulates at least two overdue tasks reading as an approval step.
Area/team overload: fires once an operational area accumulates at least six total tasks with at least two overdue.
Strategy mapped without execution: fires once a mind map has real structure (four or more nodes) but almost none of it — a fifth or fewer — is linked to an actual task.
Acquisition without conversion, decision follow-through, prepared-action-type recurrence, and Copiloto question-theme recurrence, each gated at a minimum of two qualifying occurrences.
A recurring problem stops depending on someone happening to remember the last three times it happened. The system holds that count itself. It is tied to the exact tasks, maps or decisions that produced it. Because every rule is a specific number checked against real data, a pattern that fires can always be traced back to precisely why it fired. This matters when the next step is redistributing work or having a hard conversation with a client.
A recurring problem stops depending on someone happening to remember the last three times it happened. The system holds that count itself. It is tied to the exact tasks, maps or decisions that produced it. Because every rule is a specific number checked against real data, a pattern that fires can always be traced back to precisely why it fired. This matters when the next step is redistributing work or having a hard conversation with a client.
An account has had two separate tasks blow their delivery date this quarter. Both are sitting in an approval-related stage. Handled individually, each looks like a one-off — a slow week, a busy client. Read together against the coded rule, they cross the threshold for a recurring approval-delay pattern tied to that specific client. The pattern surfaces with both dates and both task names attached. It is not just a vague sense that "this client is always slow."

No — all seven pattern rules run on fixed numeric thresholds coded directly into the engine. They are checked against real task, mind map, decision and action data.
At minimum twice for every rule. Two of the seven also require a minimum amount of underlying structure, such as a mind map with at least four nodes.
Six real places already in daily use: tasks (overdue and active), mind maps (structure and which nodes became tasks), Decision Engine history, Action Center's prepared actions, saved meetings, and direct custom events.
Custom events write to a shared, per-organization record. This is better than a single browser's local storage. A pattern one person's actions contribute to is visible to a colleague too.
No — it reports the count and the specific tasks, maps or decisions behind it. Interpreting the cause is left to the person reading it.
It is sold by subscription with a published starting price. Exact current values are on the central pricing page.
Cross-module pattern memory, in Centriu Run, reads real events from six operational surfaces — overdue and in-progress tasks, mind map structure and which of its nodes actually became tasks, Decision Engine history, Action Center's prepared actions and saved meetings, plus direct custom events — and runs each stream through a specific, hard-coded rule. It does not use a generic anomaly detector. Seven distinct pattern types can surface this way: a client whose approvals go overdue at least twice, a team area absorbing six or more tasks with at least two overdue, a mind map with real structure but almost nothing turned into a task, a funnel with acquisition activity and zero conversion activity, decisions with a track record of actually getting executed, a prepared-action type repeating at least twice, and an executive question theme coming up at least twice in the Copiloto. Nothing here is guessed by a model — every trigger is a fixed number in the code. It is checked against real data pulled live each time.