December 4, 2025 |

Unsexy And I Know It

The AI hype cycle started with flash: slick chat interfaces, viral image generators, a brand new kind of search. But as the novelty fades and institutions start treating AI like a serious long-term bet, capital is flowing toward the unsexy fundamentals. On the physical side, the numbers are staggering. Microsoft committed $80 billion to AI data centers in 2025. Meta announced $70 billion. BlackRock is in for $40 billion.. Clearly, private equity now views AI infrastructure as a distinct asset class. But there's a parallel buildout happening in operational infrastructure—the tools that make AI actually work inside organizations. MLOps funding hit $4.5 billion in 2024 and is projected to exceed $6 billion this year. AI coding tool Cursor, just raised $2.3 billion. Abridge pulled in $250 million for AI that turns doctor-patient conversations into medical documentation. One investor described it as backing "unsexy, high-utility tools that become embedded in everyday systems."

You can’t build your own data center. Not yet, anyway. But you can build the operational infrastructure that lets AI work for your business. This week's workflow shows how to build a documentation pipeline that turns your existing content into AI-ready training data.

Your documentation is valuable twice: first for humans who read it, then for AI systems that learn from it. This workflow creates a pipeline that transforms code comments and technical docs into structured knowledge bases, then feeds that content to AI chatbots that can answer customer and team questions. The result is documentation that stays current and AI that actually knows your product.

› Turn code comments and README files into customer-facing documentation automatically.

› Keep knowledge bases synchronized as your product evolves.

› Train support chatbots on accurate, up-to-date product information.

› Reduce support tickets by giving AI access to the same docs your team uses.

› Connect GitBook to your GitHub repository. Configure it to sync with your docs folder or README files, allowing GitBook to automatically pull changes when you push to main.

› Sync GitBook with Notion AI to summarize and organize GitBook content into structured pages—FAQs, feature explanations, troubleshooting guides.

› Create a dedicated workspace that serves as your single source of truth.

› Create a Chatbase chatbot and add your Notion workspace as a data source. Chatbase crawls your Notion pages and trains on the content, creating a support bot that knows your product.

› Pass formatted data to the corresponding fields in your CRM and reports.

Documentation workflows typically save 3-5 hours per week on manual updates. But the real ROI comes from downstream effects: support bots trained on accurate docs resolve more tickets without human intervention. Klarna's AI chatbot handled 2.3 million conversations in its first month—equivalent to 700 full-time agents. Even at smaller scale, the math works: fewer support tickets, faster onboarding, and AI that actually knows what it's talking about.

Want to build something like this for your business? ➡️ We can help ➡️

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