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Technical model

How the Devrix Software Manufacturing Factory Works

Business intent becomes a governed engineering system. Accountable human leaders and AI specialists produce software, evidence and reusable engineering memory through connected layers.

Intelligent technical interface

What part of the Factory would you like to understand?

Direct answer · production

Where does AI participate?

AI specialists may assist analysis, architecture, design, implementation, testing, validation, documentation, risk assessment and evidence preparation.

Participation varies by work package; autonomy is not claimed.

Open the relevant Factory layer →

Five connected layers

Intent enters. Software, evidence and memory emerge.

  1. 01

    Business and Product Intent

    Input
    Business problem, product idea, existing application, transformation goal, constraints and enterprise context
    Work
    Define the product, classify the initiative and establish capability and success models
    Output
    Product definition, scope, classification, capability model and success measures
  2. 02

    Engineering Definition

    Input
    Product definition, existing estate, quality attributes and constraints
    Work
    Connect requirements, architecture, domain, experience, APIs, data, security, integration and operations
    Output
    Controlled baseline, decisions, traceability and implementation work packages
  3. 03

    AI-Assisted Production

    Input
    Approved work packages, engineering baseline and acceptance conditions
    Work
    Human teams and AI specialists analyse, design, implement, test, validate, document and prepare evidence
    Output
    Controlled increments, technical artefacts, validation findings and evidence candidates
  4. 04

    Evidence and Quality

    Input
    Requirements, decisions, increments, risks and release conditions
    Work
    Connect coverage, testing, security, accessibility, builds, deployment and acceptance
    Output
    Evidence, known limitations, release readiness and human approval decisions
  5. 05

    Engineering Memory

    Input
    Approved programme knowledge, patterns, decisions, tests and lessons
    Work
    Qualify reusable knowledge while protecting confidential context
    Output
    Reusable components, patterns, test assets, skills, integration knowledge and methodology evolution

Responsibility boundary

AI participates. Humans remain accountable.

AI specialists may

Analyse, propose, implement, test, validate, document, assess risk and prepare evidence inside governed work packages.

Named human leaders

Direct product intent, approve critical decisions, accept risk and authorise release. AI does not independently approve enterprise software.

Two manufacturing paths

Greenfield and Product Evolution share governance—not the same starting point.

Greenfield
  1. Idea
  2. Product definition
  3. Architecture
  4. Implementation
  5. Validation
  6. Deployment
  7. Evolution
Product Evolution
  1. Existing product
  2. Capability discovery
  3. Asset and dependency analysis
  4. Preserve / improve / replace
  5. Progressive implementation
  6. Controlled migration
  7. Continued evolution

Engineering Memory

Each programme can improve the Factory itself.

Approved domain knowledge, components, patterns, decisions, test assets, skills, integration learning and lessons can become reusable engineering memory—without making confidential context public or unrestricted.

See Product Evolution in practiceSee platform manufacturing →Discuss your software →