This is Interesting to Answer
Hey everyone, happy new year 2026! I am seeking guidance for something really interesting. People here who have "hands-on experience with large-scale, enterprise-grade AI projects". I’m currently exploring AI agents, automations, and internal AI systems for companies operating under strict data protection laws and regulations. A common principle we keep hearing from regulators is: “AI should come to the data; data should not go to AI.” Meaning: * Sensitive or personal data should not be sent to third-party cloud AI tools for training * Many companies cannot use typical cloud-based automation setups (for example, hosted workflow tools like n8n cloud) This raises some practical questions when building ""AI infrastructure, Agents, and Automations"" for such companies. I’d love insights on the following: 1. What is the "practical, real-world solution" for building AI agents & automations when data cannot leave the company’s environment? 2. How do you usually "approach the architecture" (self-hosted tools, private LLMs, on-prem, hybrid, etc.)? 3. How do you "test, validate, and guarantee" that everything works securely and reliably? 4. Do companies usually "build this in-house or outsource" it (partially or fully)? 5. How do you "convince stakeholders" that the solution is compliant, secure, and future proof? 6. What "questions should we expect" from legal, IT, compliance, and leadership teams? 7. How is "pricing typically structured" for such high-responsibility projects? 8. What steps do you take to "protect yourself legally and professionally" as a builder? 9. Is it better to approach such projects as a "freelancer or an agency", and why? 10. What "legal, contractual, or compliance aspects" should absolutely not be missed? I’m genuinely looking to learn from people who’ve already been through this journey. Any frameworks, experiences, mistakes, or resources would be incredibly valuable. Thanks in advance 🙏 Looking forward to your guidance for each questions respectively.