The article explains how to design a business system by starting with the customer experience you want to create, then working backwards to the data and operational capabilities needed to support it. It offers a practical mental model: plan downwards from customer needs, but build upwards from reliable product, customer and supplier data, using deterministic systems for clear business rules and AI where judgement is less easily reduced to rules. It provides the foundation for understanding why the project’s individual components are being built, and how they are intended to fit together over time.

The article explains how to design a business system by starting with the customer experience you want to create, then working backwards to the data and operational capabilities needed to support it. It offers a practical mental model: plan downwards from customer needs, but build upwards from reliable product, customer and supplier data, using deterministic systems for clear business rules and AI where judgement is less easily reduced to rules. It provides the foundation for understanding why the project’s individual components are being built, and how they are intended to fit together over time.

The article explains why business data should be validated before it enters a database, especially when the source files are ordinary spreadsheets and reports that were never designed for automated use. It provides a practical mental model for data reliability: treat every file as unverified, test it against explicit rules and independent evidence, record disagreements, and hold uncertain data rather than guessing. It supports the project by explaining the first operational layer that makes every later calculation, recommendation and AI-assisted decision trustworthy enough to act on.

The article explains why business data should be validated before it enters a database, especially when the source files are ordinary spreadsheets and reports that were never designed for automated use. It provides a practical mental model for data reliability: treat every file as unverified, test it against explicit rules and independent evidence, record disagreements, and hold uncertain data rather than guessing. It supports the project by explaining the first operational layer that makes every later calculation, recommendation and AI-assisted decision trustworthy enough to act on.

The article explains how a distribution business can structure its database so that products, customers, suppliers, transactions, changing values, and calculated outputs remain distinct but connected. It provides a practical mental model for data design: store what happened and where each figure came from, keep changing values dated, and treat anything that can be reconstructed as derived rather than original data. It supports the project by showing how the database structure determines which operational, commercial, and customer-level questions the later system can answer.

The article explains how a distribution business can structure its database so that products, customers, suppliers, transactions, changing values, and calculated outputs remain distinct but connected. It provides a practical mental model for data design: store what happened and where each figure came from, keep changing values dated, and treat anything that can be reconstructed as derived rather than original data. It supports the project by showing how the database structure determines which operational, commercial, and customer-level questions the later system can answer.

How I used AI to build a website for my projects, articles and useful AI resources.

How I used AI to build a website for my projects, articles and useful AI resources.

The project shows how AI can turn basic Thai land-record information into a practical property-pricing and sale-cost assessment by combining official appraisal data, local geographic discovery, market evidence and tax calculations. It provides a useful mental model for where AI and conventional software each fit: AI handles ambiguous location language, search and evidence judgement, while deterministic systems handle validation and calculations that must be repeatable. The write-up is relevant because it shows how a difficult research task became a reusable sequence of AI skills rather than a one-off prompt.

โปรเจกต์นี้แสดงให้เห็นว่า AI สามารถเปลี่ยนข้อมูลพื้นฐานจากเอกสารที่ดินของไทยให้กลายเป็นการประเมินราคาขายและค่าใช้จ่ายในการขายที่นำไปใช้ได้จริง โดยเชื่อมโยงข้อมูลราคาประเมินราชการ การทำความเข้าใจพื้นที่ หลักฐานจากตลาด และการคำนวณภาษีเข้าด้วยกัน นอกจากนี้ยังช่วยอธิบายภาพรวมว่า AI และซอฟต์แวร์แบบดั้งเดิมควรทำหน้าที่ต่างกันอย่างไร AI เหมาะกับงานที่มีความคลุมเครือ เช่น การทำความเข้าใจชื่อสถานที่ การค้นหา และการประเมินคุณภาพของหลักฐาน ขณะที่ระบบแบบกำหนดกฎชัดเจนเหมาะกับการตรวจสอบและการคำนวณที่ต้องให้ผลลัพธ์สม่ำเสมอ โปรเจกต์นี้จึงมีประโยชน์ในฐานะตัวอย่างของการเปลี่ยนงานวิจัยที่ซับซ้อนให้กลายเป็น /skills ที่นำกลับมาใช้ซ้ำได้ แทนที่จะเป็นเพียง prompt ที่ใช้ได้เพียงครั้งเดียว และไม่สามารถนำมาใช้กับสถานการณ์ที่เปลี่ยนแปลงไปได้