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<!DOCTYPE html>
<html lang="pt-BR">
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<meta name="viewport" content="width=device-width, initial-scale=1" />
<title>OpenSource & método — DataBoar</title>
<meta name="description" content="Open-core BSD-3, local-first: inventário e descoberta de dados pessoais LGPD auditável, sem LLM na decisão. Método aberto — você pode auditar como o DataBoar se comporta, sem confiar numa caixa-preta." />
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<span data-lang-panel="pt-BR"><b>O compliance</b> da sua empresa mais inteligente, ágil e seguro.</span>
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Open-core & método
</div>
<h1>Aberto por design. <span class="accent">Verificável</span> por natureza.</h1>
<p class="hero-highlight">Você pode auditar como o DataBoar se comporta — sem confiar numa caixa-preta.</p>
<p class="lede">O núcleo do DataBoar é <b>open-core (BSD-3)</b>, a detecção é <b>determinística</b> e cada achado é <b>reprodutível</b>. O nosso valor não é um segredo patenteado: é execução, integração e confiança. Publicamos a receita e cobramos pelo serviço.</p>
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<a class="btn btn-primary" href="https://github.com/DataBoar/data-boar" rel="noopener noreferrer" target="_blank">Ver o código no GitHub →</a>
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<div class="section-head center">
<h2>Por que <span class="accent">aberto</span></h2>
<p>Confiança se constrói com transparência, não com marketing — ainda mais em compliance.</p>
</div>
<div class="diff-grid">
<div class="diff-card">
<div class="diff-icon c1"><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M16 18l6-6-6-6"/><path d="M8 6l-6 6 6 6"/></svg></div>
<h3>Open-core (BSD-3)</h3>
<p>O núcleo é livre: você lê, audita e roda onde quiser — inclusive air-gapped. Licença permissiva, sem amarras.</p>
</div>
<div class="diff-card">
<div class="diff-icon c2"><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M7 3h7l4 4v14H7z"/><path d="M14 3v4h4"/><path d="M9.5 13h5M9.5 16.5h5"/></svg></div>
<h3>Sem patente de software</h3>
<p>Copyright, assinatura e patente são coisas diferentes. Nós temos copyright e cobramos por assinatura — mas não patenteamos o método. Patentear um algoritmo seria como patentear um capítulo de livro.</p>
</div>
<div class="diff-card">
<div class="diff-icon c3"><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><circle cx="12" cy="12" r="9"/><path d="M8 12l2.5 2.5L16 9"/></svg></div>
<h3>IA determinística, não LLM</h3>
<p>ML (Random Forest + TF-IDF), Deep Learning e um sidecar em Clojure. É IA de verdade — só que reprodutível, verificável e sem LLM, sem generativa, sem agêntica. Sem alucinação, sem caixa-preta.</p>
</div>
<div class="diff-card">
<div class="diff-icon c4"><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M9 12l2 2 4-4"/><circle cx="12" cy="12" r="9"/></svg></div>
<h3>Cada achado é verificável</h3>
<p>Regras, validação (dígito verificador, Luhn) e referência normativa em cada resultado. Você confere a evidência — não confia num escore opaco.</p>
</div>
</div>
</div>
</section>
<section class="how-section">
<div class="wrap">
<div class="section-head center">
<h2>Um primer honesto: <span class="accent">IA ≠ LLM</span></h2>
<p>IA é o guarda-chuva. LLM, generativa e agêntica são apenas um ramo dele.</p>
</div>
<div class="diff-grid">
<div class="diff-card">
<div class="diff-icon c1"><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><circle cx="12" cy="12" r="3"/><circle cx="5" cy="6" r="2"/><circle cx="19" cy="6" r="2"/><circle cx="5" cy="18" r="2"/><circle cx="19" cy="18" r="2"/><path d="M6.5 7.2L10 11M17.5 7.2L14 11M6.5 16.8L10 13M17.5 16.8L14 13"/></svg></div>
<h3>O que usamos</h3>
<p>Modelos especializados de ML e DL, heurísticas e um sidecar determinístico em Clojure — o ramo da IA que é <b>reprodutível e auditável</b>. Potente onde importa, previsível sempre.</p>
</div>
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<h3>O que NÃO usamos no caminho crítico</h3>
<p>Nenhum LLM decide se o seu dado é sensível. Sem generativa, sem alucinação, sem depender de nuvem de terceiro para classificar. O que não é injetável pelo conteúdo do alvo vira propriedade de segurança, não só de reprodutibilidade.</p>
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<div class="diff-icon c3"><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><rect x="3" y="4" width="18" height="16" rx="2"/><path d="M3 9h18"/><circle cx="7" cy="6.5" r="0.6" fill="currentColor"/></svg></div>
<h3>Global de fábrica</h3>
<p>Multi-locale e multi-encoding de verdade: bilíngue no mínimo, qualquer idioma em minutos. O scanner entende o seu dado onde quer que a sua organização opere.</p>
</div>
<div class="diff-card">
<div class="diff-icon c4"><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M12 2l8 3v6c0 5-3.4 8.5-8 11-4.6-2.5-8-6-8-11V5l8-3z"/><path d="M9 12l2 2 4-4"/></svg></div>
<h3>Por que isso importa pra auditoria</h3>
<p>Determinístico quer dizer que um auditor (ou você) reproduz o mesmo resultado sob as mesmas versões, configuração e dados de entrada. Um classificador de LLM não garante isso: muda de resposta e não presta contas. Evidência reproduzível é o que segura numa fiscalização.</p>
</div>
</div>
</div>
</section>
<section class="usecases-section">
<div class="wrap">
<div class="section-head center">
<h2>O que devolvemos</h2>
<p>Open-core generoso: o método é público e a comunidade se beneficia.</p>
</div>
<div class="usecases-row">
<div class="usecase-item">
<div class="usecase-icon"><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M21 16V8l-9-5-9 5v8l9 5 9-5z"/><path d="M3.3 7L12 12l8.7-5M12 22V12"/></svg></div>
<div>
<h3>Wheelhouse portado</h3>
<p>Publicamos wheels para plataformas que o upstream não cobre — no-AVX, musl, hardware antigo, free-threaded — para quem roda onde os grandes não vão.</p>
</div>
</div>
<div class="usecase-item">
<div class="usecase-icon"><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M9 12l2 2 4-4"/><rect x="3" y="3" width="18" height="18" rx="2"/></svg></div>
<div>
<h3>Método publicado</h3>
<p>Padrões, validações e decisões vivem em ADRs e no código aberto — auditáveis por qualquer um, sem NDA para entender como funciona.</p>
</div>
</div>
<div class="usecase-item">
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<div>
<h3>Docs e glossário abertos</h3>
<p>Documentação bilíngue (pt-BR/en-US), glossário que alinha engenharia, compliance e procurement, ADRs, e até um laudo-testemunho de LGPD em campo — tudo aberto, sem NDA pra entender.</p>
</div>
</div>
<div class="usecase-item">
<div class="usecase-icon"><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><circle cx="6" cy="6" r="3"/><circle cx="6" cy="18" r="3"/><path d="M6 9v6"/><circle cx="18" cy="6" r="3"/><path d="M18 9a9 9 0 0 1-9 9"/></svg></div>
<div>
<h3>Correções de volta ao upstream</h3>
<p>Quando consertamos um bug ou empacotamos algo que faltava, mandamos de volta pros projetos que usamos — pacotes órfãos adotados, patches submetidos. O ecossistema que nos sustenta fica melhor.</p>
</div>
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<div class="usecase-icon"><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M7 3h7l4 4v14H7z"/><path d="M14 3v4h4"/><path d="M9.5 12h5M9.5 15.5h5"/></svg></div>
<div>
<h3>Amostras de compliance prontas</h3>
<p>Configs de framework abertas e reusáveis — LGPD, GDPR, CCPA, GLBA, e amostras pra UK/EU GDPR, PIPEDA, POPIA, APPI e PCI-DSS. Ajuste o seu perfil sem começar do zero.</p>
</div>
</div>
</div>
</div>
</section>
<section class="diff-section">
<div class="wrap">
<div class="section-head center">
<h2>Jeito <span class="accent">Unix</span>: cada ferramenta faz uma coisa — e prova</h2>
<p>Contra o monólito que faz tudo e você confia: ferramentas pequenas, composáveis e verificáveis.</p>
</div>
<div class="diff-grid">
<div class="diff-card">
<div class="diff-icon c1"><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><rect x="3" y="3" width="8" height="8" rx="1"/><rect x="13" y="13" width="8" height="8" rx="1"/><path d="M13 6h5a3 3 0 0 1 3 3v2M11 18H6a3 3 0 0 1-3-3v-2"/></svg></div>
<h3>Cada peça, um escopo</h3>
<p>O boar faz uma coisa muito bem: <b>descobrir e provar</b> onde há dado pessoal — com evidência, sem reter o dado. Mascaramento, tokenização e atendimento a direitos do titular vêm de <b>plugins</b> e ferramentas composáveis, cada uma com escopo pequeno e verificável — não uma caixa-preta que faz tudo.</p>
</div>
<div class="diff-card">
<div class="diff-icon c2"><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M12 2l9 5-9 5-9-5 9-5z"/><path d="M3 12l9 5 9-5M3 17l9 5 9-5"/></svg></div>
<h3>Você escolhe onde roda</h3>
<p>On-prem em min-spec, <b>air-gapped</b> quando a indústria e a regulação exigem, na <b>sua nuvem</b> distribuída (multi-vendor, multi-zona), ou como <b>SaaS</b> nosso. Opt-in explícito — por contrato, por audiência, por qualquer regulação.</p>
</div>
<div class="diff-card">
<div class="diff-icon c3"><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M12 3l7 3v6c0 5-3 8-7 10-4-2-7-5-7-10V6l7-3z"/><path d="M9.5 12l1.8 1.8L15 10"/></svg></div>
<h3>Não é um clone barato</h3>
<p>Não somos a versão econômica de uma suíte enterprise. Somos outra categoria: <b>aberto, determinístico, verificável</b> — e que roda onde as plataformas pesadas não vão. O escopo honesto é o produto.</p>
</div>
<div class="diff-card">
<div class="diff-icon c1"><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><rect x="3" y="4" width="18" height="16" rx="2"/><path d="M7 9l3 3-3 3M13 15h4"/></svg></div>
<h3>Sai como pipe, entra em qualquer pipeline</h3>
<p>Saída machine-readable (JSON, Excel, manifests de evidência), exit codes honestos, config-driven. O boar encaixa no teu CI, no teu SIEM, no teu GRC — não te obriga a viver dentro da nossa UI.</p>
</div>
</div>
</div>
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<div class="cta-bar-text">
<h2>Leia o código antes de confiar nele.</h2>
<p>É open-core: audite, rode local e veja por que a evidência é reprodutível.</p>
</div>
<a class="btn btn-primary cta-bar-btn" href="https://github.com/DataBoar/data-boar" rel="noopener noreferrer" target="_blank">Ir para o GitHub</a>
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Open-core & method
</div>
<h1>Open by design. <span class="accent">Verifiable</span> by nature.</h1>
<p class="hero-highlight">You can audit how DataBoar behaves — you don’t have to trust a black box.</p>
<p class="lede">DataBoar’s core is <b>open-core (BSD-3)</b>, detection is <b>deterministic</b>, and every finding is <b>reproducible</b>. Our value is not a patented secret: it is execution, integration, and trust. We publish the recipe and charge for the service.</p>
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<section class="diff-section">
<div class="wrap">
<div class="section-head center">
<h2>Why <span class="accent">open</span></h2>
<p>Trust is built with transparency, not marketing — especially in compliance.</p>
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<div class="diff-grid">
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<h3>Open-core (BSD-3)</h3>
<p>The core is free: read it, audit it, and run it where you want — including air-gapped. Permissive license, no lock-in.</p>
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<div class="diff-card">
<div class="diff-icon c2"><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M7 3h7l4 4v14H7z"/><path d="M14 3v4h4"/><path d="M9.5 13h5M9.5 16.5h5"/></svg></div>
<h3>No software patents</h3>
<p>Copyright, subscription, and patents are different things. We hold copyright and charge for subscription — we do not patent the method. Patenting an algorithm is like patenting a book chapter.</p>
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<div class="diff-card">
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<h3>Deterministic AI, not LLM</h3>
<p>ML (Random Forest + TF-IDF), Deep Learning, and a Clojure sidecar. Real AI — reproducible, verifiable, and without LLMs, generative models, or agentic stacks. No hallucination, no black box.</p>
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<h3>Every finding is checkable</h3>
<p>Rules, validation (check digits, Luhn), and normative references on each result. You inspect the evidence — you do not trust an opaque score.</p>
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<h2>An honest primer: <span class="accent">AI ≠ LLM</span></h2>
<p>AI is the umbrella. LLM, generative, and agentic systems are only one branch of it.</p>
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<div class="diff-icon c1"><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><circle cx="12" cy="12" r="3"/><circle cx="5" cy="6" r="2"/><circle cx="19" cy="6" r="2"/><circle cx="5" cy="18" r="2"/><circle cx="19" cy="18" r="2"/><path d="M6.5 7.2L10 11M17.5 7.2L14 11M6.5 16.8L10 13M17.5 16.8L14 13"/></svg></div>
<h3>What we use</h3>
<p>Specialized ML and DL models, heuristics, and a deterministic Clojure sidecar — the branch of AI that is <b>reproducible and auditable</b>. Strong where it matters, predictable always.</p>
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<h3>What we do NOT use on the critical path</h3>
<p>No LLM decides whether your data is sensitive. No generative models, no hallucination, no third-party cloud dependency for classification. Non-injectability by target content becomes a security property, not only a reproducibility one.</p>
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<h3>Global by default</h3>
<p>Real multi-locale and multi-encoding: bilingual at minimum, any language in minutes. The scanner understands your data wherever your organization operates.</p>
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<div class="diff-icon c4"><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M12 2l8 3v6c0 5-3.4 8.5-8 11-4.6-2.5-8-6-8-11V5l8-3z"/><path d="M9 12l2 2 4-4"/></svg></div>
<h3>Why this matters for audits</h3>
<p>Deterministic means an auditor (or you) can reproduce the same result under the same versions, configuration, and inputs. An LLM classifier can’t guarantee that: it changes its answer and doesn’t show its work. Reproducible evidence is what holds up under scrutiny.</p>
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</div>
</div>
</section>
<section class="usecases-section">
<div class="wrap">
<div class="section-head center">
<h2>What we give back</h2>
<p>Generous open-core: the method is public and the community benefits.</p>
</div>
<div class="usecases-row">
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<div class="usecase-icon"><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M21 16V8l-9-5-9 5v8l9 5 9-5z"/><path d="M3.3 7L12 12l8.7-5M12 22V12"/></svg></div>
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<h3>Ported wheelhouse</h3>
<p>We publish wheels for platforms upstream does not cover — no-AVX, musl, older hardware, free-threaded — for teams that run where the big stacks will not go.</p>
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</div>
<div class="usecase-item">
<div class="usecase-icon"><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M9 12l2 2 4-4"/><rect x="3" y="3" width="18" height="18" rx="2"/></svg></div>
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<h3>Published method</h3>
<p>Patterns, validations, and decisions live in ADRs and open source — auditable by anyone, no NDA required to understand how it works.</p>
</div>
</div>
<div class="usecase-item">
<div class="usecase-icon"><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M4 4a2 2 0 012-2h9l5 5v13a2 2 0 01-2 2H6a2 2 0 01-2-2z"/><path d="M8 8h6M8 12h8M8 16h5"/></svg></div>
<div>
<h3>Open docs and glossary</h3>
<p>Bilingual documentation (pt-BR/en-US), a glossary aligning engineering, compliance, and procurement, ADRs, and even a field LGPD witness report — all open, no NDA to understand it.</p>
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</div>
<div class="usecase-item">
<div class="usecase-icon"><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><circle cx="6" cy="6" r="3"/><circle cx="6" cy="18" r="3"/><path d="M6 9v6"/><circle cx="18" cy="6" r="3"/><path d="M18 9a9 9 0 0 1-9 9"/></svg></div>
<div>
<h3>Fixes back upstream</h3>
<p>When we fix a bug or package something that was missing, we send it back to the projects we rely on — adopted orphan packages, submitted patches. The ecosystem that supports us gets better.</p>
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<h3>Ready-made compliance samples</h3>
<p>Open, reusable framework configs — LGPD, GDPR, CCPA, GLBA, plus samples for UK/EU GDPR, PIPEDA, POPIA, APPI, and PCI-DSS. Tune your profile without starting from scratch.</p>
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<div class="wrap">
<div class="section-head center">
<h2><span class="accent">Unix</span> way: each tool does one thing — and proves it</h2>
<p>Against the monolith that does everything and asks for trust: small, composable, verifiable tools.</p>
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<div class="diff-grid">
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<h3>One scope per piece</h3>
<p>The boar does one thing very well: <b>discover and prove</b> where personal data lives — with evidence, without retaining the data. Masking, tokenization, and data-subject rights come from <b>plugins</b> and composable tools, each with a small verifiable scope — not a black box that does everything.</p>
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<h3>You choose where it runs</h3>
<p>On-prem on min-spec, <b>air-gapped</b> when industry and regulation require it, in <b>your cloud</b> (multi-vendor, multi-zone), or as <b>our SaaS</b>. Explicit opt-in — by contract, by audience, by any regulation.</p>
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<h3>Not a cheap clone</h3>
<p>We are not the discount version of an enterprise suite. We are another category: <b>open, deterministic, verifiable</b> — and it runs where heavy platforms will not. Honest scope is the product.</p>
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<h3>Outputs like a pipe, fits any pipeline</h3>
<p>Machine-readable output (JSON, Excel, evidence manifests), honest exit codes, config-driven. The boar plugs into your CI, your SIEM, your GRC — it doesn’t force you to live inside our UI.</p>
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<h2>Read the code before you trust it.</h2>
<p>It is open-core: audit it, run it locally, and see why the evidence is reproducible.</p>
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