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AI Driven Development

AI builds. People verify.
We call it ADD.

Why ADD

Now that AI writes the code, what is left for developers?

AI writing code is no longer an experiment.

Yet most organizations still fail to put it to work.
Two walls stand in the way.

  • Misconception

    “AI will handle all of it.”

    Setting direction and judging quality remain human work.

  • Doubt

    “How can we trust code an AI wrote?”

    Without answers on control and accountability, adoption stalls.

What is needed is not a new tool but a new division of work between AI and people.

Workflow

How the workflow changes

Before

With ADD

  1. 01Requirements Unchanged Requirements
  2. 02Design Changed Brief the AI
  3. 03Development Changed AI generates code
  4. 04Testing Changed Developer verifies and revises
  5. Added Iterate with AI
  6. 05Release Unchanged Release

What AI Does

What AI takes on

The more repetitive and structured the task, the faster AI handles it.

  • Requirements analysis

    Document cleanup, feature breakdown

  • UI development

    Component generation

  • API development

    CRUD code generation

  • Database design

    Draft ERD

  • Testing

    Unit test generation

  • Documentation

    README and API documentation

  • Code review

    Improvement suggestions

  • Refactoring

    Structural improvement

Accountability

Speed from AI, quality from people

ADD does not remove the developer. It raises the stakes of the role.

AI output does not ship as is. Judging what is correct, deciding what to keep and owning the result stay with people.

  1. Writing code

The developer had one job.

  1. Define the problem
  2. Brief the AI
  3. Verify the result
  4. Design the architecture

Design, judgment, verification and quality management carry far more weight.

QA Discipline

Built fast, trusted just as fast

Developing with AI speeds up the early stages.
That gain is often lost again at verification.
We hit that wall ourselves, and built a way through it.

Plenty of companies build fast with AI.
Few can make what they built trustworthy just as fast.

  1. 01

    Verification by AI too

    Every request that comes in is reconciled unattended overnight to confirm none was missed, and anything skipped is queued again automatically. The AI that builds and the AI that verifies are kept separate, so every result is cross-checked by a different model.

  2. 02

    The bar for done is set in advance

    What counts as done is defined as a gate before work starts. With one wording for verdicts and one owner for the record, “almost there” stops being an answer.

  3. 03

    How far it was checked is on the record

    Verification is recorded in three levels, static, specification and actual run. Results of an actual run are kept together with measured evidence such as logs, data or test output, so how far something was confirmed stays visible in the record.

  4. 04

    Independent verification before conclusions

    Critical calls are cross-checked through a second path before they are made. Irreversible work requires all three: independent verification, a confirmed rollback path and human approval.

  5. 05

    Quality moves upstream

    Automated checks run the moment code changes, then pre-commit rules and a pre-release quality gate filter in turn. Most issues are gone before QA ever sees them.

The know-how lives in automated rules, gates and checklists rather than in someone's instinct. It survives staff changes, and the mistakes we already paid for are not repeated on client projects.

Skills

Skills that matter

ADD calls for people who can direct and verify AI, not people who write the most code.

  • 01

    Requirements analysis

    Defining clearly what needs to be built

  • 02

    Prompting

    Giving AI instructions specific enough to leave no room for misreading

  • 03

    Architecture judgment

    Deciding overall structure and technology choices

  • 04

    Code review

    Verifying the quality and safety of AI-generated code

  • 05

    Verification design

    Setting the bar for done up front, then automating the check

  • 06

    Domain knowledge

    Connecting business requirements to technical decisions

In Practice

How we build

ADD is not a document at COMPUTERMATE. It is how we build.
We apply AI on top of 34 years on the manufacturing floor, and every solution we ship is built this way.

Our internal developer portal, byDP, runs the full ADD workflow in production, including AI code review and AI quality management.
We use what we build before anyone else does.