KloudStack/healthcare

Method

Delivery, bounded by evidence

We ship AI systems through Pareto-Driven Development: find the vital few, build the smallest complete slice, measure it, then earn the next increment.

Core thesis

A development method, not an 80/20 slogan.

Spec-led

Work starts with a short spec that fixes the outcome, the constraints and the vital-few hypothesis, before any build begins.

Evidence-led

Priorities are hypotheses. They are tested against usage, delay, defects, cost and risk, not opinion.

Complexity-bound

Services, abstractions and extra automation are added only when measured evidence shows the simpler version cannot meet the target.

The loop

  1. 01

    Frame

    Fix the outcome, the constraints and what must not be traded away.

  2. 02

    Map

    Find where value, effort and risk are concentrated.

  3. 03

    Select

    Choose the few changes expected to move the outcome most.

  4. 04

    Deliver

    Build one complete, working slice end to end, not a pile of partial parts.

  5. 05

    Measure

    Collect real evidence against the baseline, not a proxy for it.

  6. 06

    Decide

    Expand, simplify, pivot or stop. The loop always ends in a decision.

The loop ends in a decision, not another backlog entry.

A deliberately uneven roadmap

If everything is Now, the method has not been applied.

Now

The vital few, built as a complete, working slice.

Next

Conditional improvements the evidence already supports.

Later

Valid ideas, waiting on evidence or capacity, with a review date.

Not now

Low-value or unearned complexity, named and set aside on purpose.

What it never trades away

PDD optimises inside the boundary. It does not ask which safeguard can be skipped to hit a number.

BoundaryWhat it means in practice
SecurityThreat controls and least privilege are never traded for speed or scope.
PrivacyData minimisation and access rules are fixed before anything is built.
AccessibilityAn inclusive experience is a requirement, not a stretch goal.
ComplianceLegal and regulatory obligations are treated as fixed constraints.
Data integrityCorrectness, backup and recovery are never the first thing cut.

Worked example

Applied to one of our own delivery pipelines.

The outcome: cut standard deployment lead time from hours or days to under 45 minutes, while keeping full production control.

Vital few
One deployment path, automated quality gates and a canary rollback, instead of a wider platform rebuild.
Complete slice
Build, scan, staging tests, approved canary, metrics, promote or rollback, audit evidence.
Measures
Lead time, failure rate, rollback time, hands-on minutes, cost per successful release.

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