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.

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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

Hypothesis and Experiment Prioritization Automation: Testing Cheap Instead of Guessing Expensive

A backlog of "things we could try" tends to get worked in whatever order feels most urgent that week. This is different from the order that would teach the most for the least cost. Centriu Oracle's lab turns every idea into a structured hypothesis. This includes a statement, its origin, supporting evidence, and an expected metric. It is scored on four numbers a person assigns directly: impact, ease, reach, and confidence. Each is 1 to 5 (reach up to 10). From those four numbers the tool computes an ICE score. It also computes a reach-weighted RICE score. There is a separate risk-times-learning score. It classifies the hypothesis into one of four quadrants. These are quick win, big bet, fill-in, or thankless. This is done by crossing impact against ease. Every hypothesis that gets tested spawns one or more typed experiments. Every experiment is required to close with an explicit decision: escalate, iterate, pivot, or kill. So a finished test can never just quietly disappear from view without anyone recording what was learned.

Quero resolver isso

Why "test everything" backlogs rarely test the right thing first

Once a team accepts that testing cheap beats guessing expensive, the next problem is deciding what to test first. A backlog with no consistent scoring system tends to get worked in whatever order feels most urgent, most exciting, or most politically convenient that particular week. A genuinely high-impact idea that happens to be hard to execute competes unfairly against an easy, low-impact idea that just happens to be quick to ship. Without a forced, closing decision on every finished experiment, the backlog also fills with tests nobody ever circled back to interpret.

Once a team accepts that testing cheap beats guessing expensive, the next problem is deciding what to test first. A backlog with no consistent scoring system tends to get worked in whatever order feels most urgent, most exciting, or most politically convenient that particular week. A genuinely high-impact idea that happens to be hard to execute competes unfairly against an easy, low-impact idea that just happens to be quick to ship. Without a forced, closing decision on every finished experiment, the backlog also fills with tests nobody ever circled back to interpret.

Quero resolver isso

How Centriu Oracle's lab scores hypotheses and closes the loop

A hypothesis is created with a required statement. It also has optional origin, supporting evidence, an expected metric, and an expected outcome. There are four scoring inputs a person assigns directly: impact (1-5), ease (1-5), reach (1-10), and confidence (1-5). There is also a required risk level (low, medium, high, or critical). From those inputs, a pure scoring function computes an ICE score (impact times confidence times ease, 1 to 125). It also computes a reach-weighted RICE score (reach times impact times confidence, divided by the inverse of ease). A PIE score is kept for record alongside ICE. There is a separate risk-learning score (the risk level's weight multiplied by six minus confidence). This score surfaces hypotheses where testing would teach the most. This is precisely because confidence is currently low. The same inputs classify every hypothesis into exactly one of four quadrants. This is done by crossing impact against ease. The quadrants are: quick wins (high impact, high ease), big bets (high impact, low ease), fill-ins (low impact, high ease), or thankless (low impact, low ease). These are plotted on a visual matrix. So a backlog's shape is visible at a glance, not buried in a sorted table. A hypothesis moves through six explicit states: proposed, prioritized, in experiment, validated, refuted, or archived. Each experiment created against it declares one of seven kinds (A/B test, paid channel, page/landing, offer, message/copy, qualitative, or other). It also declares a design, audience, channel, primary metric, and explicit success and failure criteria. These are declared up front, before the test runs. Every experiment is required to close with one of six decisions: pending, escalate, iterate, pivot, kill, or no action. This is alongside a result, an interpretation, and recorded learnings. So a finished test can never just silently vanish from the backlog without anyone stating what was concluded. Before any experiment is saved, the system verifies server-side that its declared hypothesis genuinely belongs to the same project and organization the experiment is being created in. This is a cross-object integrity check. It stops a hypothesis from one engagement from silently absorbing an experiment meant for another.

Ver como funciona

What is actually built today

Four scoring dimensions per hypothesis: impact, ease, reach, confidence. These feed three parallel scores (ICE, RICE, PIE). Plus a distinct risk-learning score that specifically surfaces low-confidence, high-uncertainty ideas worth testing to learn, not just to win.
An automatic impact-times-ease quadrant classification. This includes quick wins, big bets, fill-ins, thankless. It has a dedicated visual priority matrix. This is confirmed directly in the classifier function's own source.
A six-state hypothesis lifecycle: proposed, prioritized, in experiment, validated, refuted, archived. This keeps a tested idea's current status visibly distinct from one still waiting.
Seven typed experiment kinds: A/B test, paid channel, page/landing, offer, message/copy, qualitative, other. Each declares success and failure criteria up front, before the test starts.
A mandatory closing decision on every experiment. This can be escalate, iterate, pivot, kill, or explicitly no action. So a finished test always leaves a recorded conclusion behind.
A server-side referential-integrity check confirmed in the experiment-save function itself. An experiment can only be attached to a hypothesis that verifiably belongs to the same project and organization. It is never assumed from client input.
Confirmed distinct from wave 12's consulting-plan priority formula and wave 21's market-signal priority formula. This scoring system applies specifically to unproven, testable hypotheses. It uses a different formula set (ICE/RICE/PIE plus risk-learning) than either prior page.

Conhecer os recursos

What changes operationally

A testing backlog gets worked in an order two different people would actually agree on. This is because the scoring inputs and the resulting math are the same for everyone. It is not a matter of individual gut feeling. A bold, uncertain idea and a safe, incremental one both get a fair, differentiated hearing. This is instead of the safer idea always winning by default. And a finished experiment always leaves behind an explicit decision and a recorded learning. So the same already-tested idea never quietly resurfaces as if it were new.

A testing backlog gets worked in an order two different people would actually agree on. This is because the scoring inputs and the resulting math are the same for everyone. It is not a matter of individual gut feeling. A bold, uncertain idea and a safe, incremental one both get a fair, differentiated hearing. This is instead of the safer idea always winning by default. And a finished experiment always leaves behind an explicit decision and a recorded learning. So the same already-tested idea never quietly resurfaces as if it were new.

Quero esses resultados

A backlog of five ideas gets an honest order (illustrative scenario, not a real client)

A consultant logs a bold repositioning idea. It has impact 5, ease 2, reach 8, confidence 2. This is alongside a small landing-page copy tweak. It has impact 2, ease 5, reach 9, confidence 4. On ICE alone (impact times confidence times ease) the repositioning idea scores 20. The copy tweak scores 40. The "smaller" idea actually wins on pure ICE. This is because low confidence and low ease both drag the bolder idea down. But the risk-learning score tells a different part of the story. The repositioning idea's low confidence (2) against a high declared risk level pushes its risk-learning score well above the copy tweak's. This means testing it cheaply would resolve real uncertainty. It would not just add a small, well-understood win. The quadrant view shows the repositioning idea as a "big bet" (high impact, low ease). It shows the copy tweak as a "quick win" (low impact, high ease) side by side. Both are worth doing. This is for genuinely different reasons the raw ICE ranking alone would have obscured.

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How to get started

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Frequently asked questions

What is the difference between ICE, RICE, and the risk-learning score?

ICE multiplies impact, confidence, and ease. RICE additionally weights by reach and divides by the inverse of ease. This favors ideas that touch more people. The risk-learning score is separate again. It multiplies the hypothesis's risk-level weight by (6 minus confidence). This specifically surfaces low-confidence, higher-risk ideas where testing would resolve real uncertainty.

What are the four priority quadrants?

Quick wins (high impact, high ease), big bets (high impact, low ease), fill-ins (low impact, high ease), and thankless (low impact, low ease). These are computed automatically from the same impact and ease scores used in ICE.

Can an experiment be left open indefinitely without a conclusion?

It can stay in "planned" or "in progress" status. But the decision field defaults to "pending". It is a required, explicit field on every experiment. The interface makes an unresolved decision visibly distinct from a genuinely closed one.

Can an experiment be attached to a hypothesis from a different project?

No. Before saving, the system verifies server-side that the declared hypothesis belongs to the exact same project and organization as the experiment being created. A mismatch is rejected, not silently allowed.

Is this the same prioritization formula used elsewhere in Oracle?

No. Oracle's consulting-plan feature and its market-signal-radar feature each use a different formula. They are applied to different kinds of records (plan items and market signals, respectively). This ICE/RICE/PIE/risk-learning set is specific to testable hypotheses.

What does Centriu Oracle 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 Oracle scores and closes the loop on testable hypotheses

Quero resolver isso