Skip to content
Language
日本語English
Theme
MIIDAS CO., LTD.

An HTML dashboard platform

BI tools meant repetitive GUI work with little flexibility and no review trail, so I built a delivery form where requirements and data definitions are written in Markdown and generative AI produces the implementation.

CoreData EngineeringData Platform EngineeringBI & Data VisualizationGenerative AI DevelopmentWeb Application DevelopmentBusiness Process Improvement & Automation
Organisation
MIIDAS CO., LTD.
Period
Mar 2026 – Present
How it started
Assigned (My manager asked me to trial it)
My part
Architecture, implementation, and coordination with the using divisions, alone
For whom
Sales planning, CS, business development and science teams
What changed
Repeating GUI work inside the limits of a BI tool → Hand over requirements and data, AI writes the implementation, and every change is reviewed and recorded in Git

What I did

  • Standardised the purpose and requirements of a dashboard into a format that leaves a record (Markdown).

  • Built the delivery path from query definition through Lambda, Google Drive, Apps Script and Google Sites.

What comes next

  • Moving them onto the internal application platform as containers, reading from Redshift directly.

  • Folding authentication into the shared single sign-on.

  • Settling on standards for controls and layout.

  • Three issues are known and open: data passed indirectly through CSV, the time requirements and data preparation take, and the governance the added flexibility demands.

Choices I made

  • Replaced GUI work in the BI tool with requirements and data definitions written in Markdown, leaving the implementation to generative AI. Changes get reviewed in Git and leave a trail.

Tools

Languages
SQL / JavaScript / HTML / CSS / Google Apps Script
Cloud & Infrastructure
AWS (Lambda)
Tools
Google Drive / Google Sites

Related strengths

  • Extraction that goes inside the implementation, visualisation that serves the business goal.

    I work alongside the business from the definition of the metric itself, and stay with it through to the insight it was asked for. From IoT systems to web and mobile, I have broad experience of building software, so I extract data with a deep understanding of the implementation behind it. Visualisation and analysis are the same — I put generative AI to work and deliver quickly, in the form the person will actually use. Underneath that, I can shape the data, the context around it and the ETL that feeds it for the age of generative AI, so that people and AI alike can work with what is there.

Related traits

  • I can design the whole system, not one layer of it

    From hardware up to the cloud, I can draw the blueprint. I have built IoT hardware, web applications, mobile apps, and a game service platform running in the cloud, so I can combine pieces I have not used before and still see how the whole thing fits together. Holding that picture is also what lets me hand implementation to generative AI and still carry a whole system on my own.

  • I put generative AI to full use

    At work and on my own projects alike, generative AI does most of the building. Claude Code and a task manager I built myself let me run several projects at once while I mostly review. I deliberately do not review implementation line by line; I review the design closely instead, so that what gets built is still aimed at the goal. The same applies outside code — I have been learning to produce game assets and short marketing videos with tools like ElevenLabs.

Back to all projects