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

Fabrication-Pattern Regression-Guard Automation: A Script That Hunts the Exact Shape of Nine Real Defects

After an internal audit found the same failure pattern in nine places across Centriu Axiom's code — a feature not ready returning a plausible-looking number instead of saying it didn't know — the fix included a standalone script. This script makes sure none of those nine patterns can quietly come back. The script scans every source file in the module's service, hook, page, and component directories for the exact code signatures each defect left behind: a random-number function feeding into a score or pass/fail decision, the exact hash algorithm used for the fake "proof" hash, a fixed-delay timer with no real work behind it, a path pointing at a storage scheme that was never used, and severity or risk rankings computed via array lookup that treats an unrecognized value as safe. Running it today confirms it still passes cleanly, scanning 262 files. Its origin story includes a real self-correction: the first time it was attacked by reintroducing the fixed defects one at a time, one rule failed to catch its target. This happened not because the pattern was wrong, but because the script's comment-stripping step had erased the exact comment the rule was looking for.

Quero resolver isso

Why finding nine instances of the same bug once is not the same as preventing a tenth

Fixing nine real occurrences of the same defect pattern is real, valuable work. But a fix applied nine times by hand says nothing about whether a tenth instance, written by someone unaware of the previous nine, would be caught before it ships. A pattern that happened nine times in one codebase shows something structural about how that pattern gets introduced, not nine unrelated coincidences. The only way to close that gap is a mechanism that keeps checking for the pattern itself, automatically. This is better than relying on everyone who touches the code to remember nine specific past incidents.

Fixing nine real occurrences of the same defect pattern is real, valuable work. But a fix applied nine times by hand says nothing about whether a tenth instance, written by someone unaware of the previous nine, would be caught before it ships. A pattern that happened nine times in one codebase shows something structural about how that pattern gets introduced, not nine unrelated coincidences. The only way to close that gap is a mechanism that keeps checking for the pattern itself, automatically. This is better than relying on everyone who touches the code to remember nine specific past incidents.

Quero resolver isso

How Centriu built a guard against the exact pattern the audit found, and tested it against itself

The same internal audit that changed Axiom's proof-of-action mechanism and connected its safety scanner to the real AI path produced a standalone script. This script's purpose is to refuse to let any of the nine specific fabrication patterns it found come back anywhere in the module. Instead of judging intent, the guard searches for the concrete signatures each defect left in the code.

Among the checks: any code where a random-number function feeds directly into a variable used as a pass/fail decision, a score, or a count shown to a user as a measurement. There is a short, justified allowlist for legitimate uses of randomness, like generating an internal identifier. Each exemption has its reason written down. This is because an exemption without a documented reason is the same failure mode as the original defect: someone decided, and nobody else knows why. The precise bit-shifting pattern of the specific homemade hash algorithm the fake "proof" feature used is also checked, plus any comment admitting a hash implementation is a placeholder. The guard checks any storage path referencing the fake `mock://` scheme. It also checks any severity or risk ranking computed by looking up a value's position in an array. This is the exact fail-open pattern that let an unrecognized threat severity silently rank as harmless in the Safety Scanner. The script also checks that specific wiring stays intact going forward. This includes that the AI conversation path still calls the safety scanner's real enforcement function, that action execution still calls the proof-capture functions, and that a set of legacy SQL migration files still carry their obsolete-and-do-not-apply marker.

The script's honesty about its limits is important: it strips out comments before scanning code. This is because several of its own comments quote the bad code being guarded against. Without stripping comments first, the guard would flag its own documentation of the fix as evidence the defect had returned. This would create a bad incentive, punishing whoever explained the fix in a comment.

That design decision was tested, and the test found a real gap in the guard's first version — not a hypothetical one. One rule was built to catch a fixed-delay timer used to fake real processing time. Its detection method searched for the specific comment associated with that pattern ("Simulate LLM delay"). But the guard's comment-stripping step ran first, and it removes that kind of comment before any rule sees the code. So when the fixed delay was reintroduced without its identifying comment, as part of testing the guard by trying to sneak 13 previously-fixed violations back in one at a time, that rule missed it. The other 12 reintroduced violations were all caught. The rule was rewritten to search for the delay's actual executable PATTERN — a timer call with a fixed delay above a minimum threshold — instead of relying on a comment that the script had already deleted. Re-testing after the fix caught all 13 reintroduced violations.

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What is actually built today

A standalone script scanning every `.ts`/`.tsx` file in the module's service, hook, lib, page, and component directories. This was confirmed by running it today to still pass cleanly across 262 files.
Detection for a random number feeding directly into a score, pass/fail decision, or count shown as a real measurement. There is a short, justified allowlist for legitimate uses of randomness.
Detection for the specific homemade hash pattern used in the original fake "proof" feature, plus any comment admitting a hash implementation is a placeholder.
Detection for a fixed-delay timer pattern used to fake real processing time. This was fixed after a self-test found the original version relied on a comment its own comment-stripping step had already deleted.
Detection for the fake storage-path scheme the original fabricated evidence used, and for severity/risk rankings computed via array lookup that treat an unrecognized value as safe.
Direct checks that the safety-scanner enforcement function and the proof-capture functions stay wired into their real call sites. This also checks that specific legacy SQL files keep their obsolete-and-do-not-apply marker.

Conhecer os recursos

What changes operationally

The nine specific defects found in one audit are now backed by a standing, repeatable check for the pattern itself. This is better than just nine individual fixes that could be quietly undone or reintroduced elsewhere without anyone noticing. Because the guard was tested against its own targets and found a real gap in its first version, its current pass result is more important than a check that has only ever been run against code nobody tried to sneak a violation past.

The nine specific defects found in one audit are now backed by a standing, repeatable check for the pattern itself. This is better than just nine individual fixes that could be quietly undone or reintroduced elsewhere without anyone noticing. Because the guard was tested against its own targets and found a real gap in its first version, its current pass result is more important than a check that has only ever been run against code nobody tried to sneak a violation past.

Quero esses resultados

A reintroduced shortcut caught before it ships (illustrative scenario, not a real client)

A future change to the code, made by someone with no memory of the original nine-defect audit, reintroduces a random-number-based placeholder score somewhere new. This person plans to replace it with a real calculation "later." Running the guard script catches this immediately and by name. It does not let it ship silently like the first nine instances did before anyone was looking.

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How to start

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Frequently asked questions

What exactly does this script check for?

The specific code signatures nine real, independently-found defects left behind: a random number feeding a score or decision, a particular homemade hash pattern, a fixed theatrical delay, a fake storage path, and severity rankings that treat an unrecognized value as safe.

Does the script still pass today?

Yes — re-run directly against the current code, it scans 262 files and reports no violations. This confirms the original fixes have held for close to two months.

What gap did the guard's own self-test find?

One rule searched for a specific comment to detect a fake processing delay. But the guard's comment-stripping step had already removed that comment before the rule could see it. So reintroducing the delay without its comment slipped through undetected until the rule was rewritten to also check the actual delay pattern.

Does this guard run automatically on every commit or pull request?

No — direct inspection of the repository found no CI workflow or `package.json` script invoking it. It exists today as a standalone script someone runs directly, not an automatic gate.

Does the guard flag every use of Math.random() as a violation?

No — it specifically targets randomness feeding a score, pass/fail decision, or count shown as a measurement. There is a short, documented allowlist for legitimate uses like generating an identifier.

What does Centriu Axiom 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 Axiom guards against fabricated results reappearing

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