Anyone who needs 6 months for an AI project today has already lost — not because of the competition, but because of the learning curve. In the last 18 months, I've built more working products than in the 10 years before.
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Co-preneurship means: we act as co-founders on demand — with speed, ownership and early prototypes. Learn how Reruption delivers AI projects faster and with better risk awareness.
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Get productive quickly with AI while building systems that remain maintainable over the long term: We outline architecture principles, deployment paths and practical examples so AI products work today and stay scalable tomorrow.
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How do you create copilots that act like dependable domain experts rather than creative chatbots? We present a technical and methodological framework with concrete patterns.
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Practical guide to secure, audit-proof AI systems in enterprises: Tenant Isolation, data classification, audit trails, model versioning, and our architecture with Hetzner, Coolify, Postgres and an internal AI proxy.
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How companies can build their own LLM clusters on Hetzner: step-by-step architecture, SaaS vs. self-host cost comparison, deployment with Coolify and best practices for security and auditability.
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Many enterprise solutions fail with Retrieval-Augmented Generation: irrelevant sources, hallucinations, lack of revision control. We show how Domain Knowledge Capture delivers reliable, auditable knowledge models.
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We give a look into the PostFlow lab: how we test **content psychology**, **data models** and **sequences** with 5,000+ of our own LinkedIn posts — and how these insights flow directly into lead generation, employer branding and personal branding.
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The Mittelstand is in the midst of the transition to autonomous AI systems. We explain why traditional consulting fails, how our 3‑week AI PoC works and how companies can operationalize AI step by step.
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How mechanical engineering in the Stuttgart region can radically accelerate production, service and quotation in 2025 with agentic systems, private LLMs on Hetzner and domain-specific knowledge models.
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Programmatic UX describes a new generation of interfaces: AI-generated, adaptive surfaces that shape themselves according to role, intent and data. We explain concepts, architecture and practical examples.
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A single internal LLM can be more than a tool: it becomes the central infrastructure for knowledge bots, process copilots, autonomous workflows and decision engines. We outline architecture, security and change management.
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In 2025, more and more SMEs choose their own domain-specific LLMs instead of external APIs. Why this is more secure, cheaper and more business-relevant — with architecture, tools and a practical roadmap.
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Real management consulting in 2025 means: we deliver software, not just PowerPoints. How AI engineering, the Co-Preneur principle and rapid PoCs resolve decision blockages and create real value.
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Many copilot projects in the mid‑market fizzle out: too broad a scope, poor UI, unreliable answers. We analyze the typical mistakes and show concretely how Reruption builds robust, trustworthy copilots.
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Dashboards provide data. We need systems that decide, act, and automate work. This post shows how LLM-based workflows steer decisions — with architecture, observability and risk management.
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We explain why Reruption consistently develops AI products with Python SSR and Jinja2: faster cycles, lower complexity, better debuggability, and stronger AI integration for long‑living prototypes.
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How prioritization decides success: a practical framework for AI projects, including heatmaps, use-case portfolios and decision trees — based on Reruption experience.
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Programmatic SEO 2025: How we use Python SSR (Jinja2), AI research, strict quality frameworks and Coolify to produce 10k–50k high-quality landing pages — predictable, scalable, measurable.
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Our 21-Day AI Delivery Framework brings AI ideas into production within three weeks. This deep dive walks through each week, showing how we distill, harden, and launch use cases.
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