Discovery-to-Decision

Discover, then clear, then keep.

D2D is the warehouse for a US healthcare company. The service is ongoing. AI writes the first draft of each step from the system the company already runs.

  • Knowledge discovery

    We read visits, charges, claims, and patients. The practice system already wrote them.

  • De-cluttering

    We keep what a count needs. We leave the rest out.

  • Requirement finalizing

    AI proposes one shape. The expert confirms it.

  • Building

    That confirmed shape becomes the pipeline. We deploy the draft.

  • Maintaining

    The service is ongoing. A new day starts a new question.

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  1. The script · Observe

    DiscoverThe lamp is on the manuscript. Inquiry makes it visible.

  2. The tree · Analyse

    ClearEach part maps back to its source.

  3. The yantra · Synthesise

    ConfirmOne center. People make the decision.

  4. Solving problems · Act

    BuildThe tangle becomes visits and money.

  5. The return · Act

    KeepThe light goes back to the source. We observe again.

The deployment · Azure

  • SQL Managed Instance
  • Data Factory
  • Batch
  • Blob Storage
  • Python

SQL holds the warehouse. Data Factory starts each load. Batch runs it in Python. Blob storage carries the engine.

AI wrote the first draft of each step. The expert corrected it. We deployed that draft.

Discovery-to-Decision for a US healthcare company. The service is ongoing. Five phases follow the film: discover, clear, confirm, build, and keep. The load runs on Azure SQL Managed Instance, Data Factory, Batch, Blob Storage, and Python. AI wrote the first draft of each step. The expert corrected it. We deployed that draft.