{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What is AI workflow automation and agentic ops?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "AI workflow automation and agentic ops means redesigning a business workflow first, then using AI agents, integrations and guardrails to remove manual handoffs without losing human control. The work starts with the process: CRM updates, support routing, finance checks, reporting, lead handling, SEO operations or internal approvals. Weak automation copies a broken process into software. Strong automation compresses the process, removes steps that should not exist, then connects the remaining work to the systems the team already uses. The goal is not a chatbot. The goal is a reliable operating layer that reduces repetitive work, shows exceptions clearly and keeps approval points visible."
      }
    },
    {
      "@type": "Question",
      "name": "What is AI-native product engineering?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "AI-native product engineering means building software where AI is part of the product architecture, not a feature added at the end. That includes copilots, RAG systems, browser agents, WhatsApp agents, AI dashboards, internal tools and AI-enabled mobile or web apps. The important work is not only prompting the model. The product needs data boundaries, retrieval quality, fallback behavior, latency control, evaluations, security rules and human review paths. A demo can look useful in a week and still fail in production. AI-native engineering is the discipline that turns the useful demo into a system users can trust under real load."
      }
    },
    {
      "@type": "Question",
      "name": "What is a vibe-code MVP launch review?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "A vibe-code MVP launch review is a technical audit for products built quickly with tools like Cursor, Lovable, Bolt, Replit, Claude, Codex or Copilot. The review checks whether the AI-generated code can survive real users, real data and technical diligence. It looks at authentication, permissions, data integrity, API behavior, deployment, logging, security, dependency risk, tests and failure cases. The point is not to shame fast prototyping. Fast building is useful. The risk starts when a prototype becomes a business system without anyone checking the parts the generator skipped, misunderstood or hardcoded."
      }
    },
    {
      "@type": "Question",
      "name": "What is an AI enablement retainer?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "An AI enablement retainer is recurring technical and operational support for companies that want AI adoption to keep improving after the first pilot. It covers workflow selection, process redesign, tool rollout, governance, prompt and eval systems, team training, automation maintenance and review of new use cases. Most AI initiatives stall because one useful experiment never becomes a managed operating habit. A retainer keeps the work moving: deciding what should be automated next, checking what is already live, measuring quality, protecting data boundaries and making sure the team can actually use the system without depending on one person."
      }
    }
  ]
}