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Axiotech Automation Ltd
+44 (0)7415 112 892
sales@axiotech.co.uk

Faster engineering with retained accountability

Use AI to accelerate engineering work without weakening review, source control or release authority.

Axiotech applies AI assistance within a conventional software-engineering control system: approved inputs, repositories, requirements, review, automated checks, test evidence, dependency control and human release decisions.

Scope this requirement See all engineering services

Good fit

Use this service when

  • Engineering applications, integration services, test tooling and bounded controls-support tasks with reviewable outputs.
  • Teams wanting a policy, pilot workflow, repository controls or a defined development work package.

Scope boundary

Not assumed or silently included

  • Unreviewed AI-generated code released to a machine or regulated system.
  • Uploading customer source, credentials or controlled documents to services without approved data handling.

Why act

The operational risk behind the technical symptom

The first work package is shaped around reducing an explicit risk or enabling a named decision.

01

Plausible output bypasses engineering understanding and introduces hidden defects.

02

Generated dependencies or copied patterns create licence, security and maintainability exposure.

03

Faster code creation increases the review and test bottleneck instead of reducing delivery time.

Purchasable entry points

Choose a bounded engagement before expanding scope

Final inclusions, dependencies, location, schedule and commercial terms are confirmed in a formal quotation.

Package 1

AI development-control assessment

Decision enabled: An approved boundary, tool position and pilot plan.

Typical scope: Information classes, use cases, repositories, review, testing, dependency and audit requirements.

Package 2

Governed pilot work package

Decision enabled: Measured evidence from a bounded engineering backlog.

Typical scope: Baseline task set, controlled workflow, implementation, review/test results, exceptions and lessons.

Package 3

Engineering enablement

Decision enabled: A repeatable team process with controls and evidence.

Typical scope: Guidance, templates, repository checks, evaluation, training and periodic control review.

Artefact manifest

What remains after the engineering work

The exact document set scales with risk and the approved work package.

  • Approved-use, data-handling and authority matrix
  • Prompt/context and source-provenance guidance
  • Repository, review and dependency-control workflow
  • Test/evaluation evidence and exception record
  • Release criteria, training and periodic-review plan

Technical and responsibility boundaries

Make ownership reviewable

  • AI output remains an input to accountable engineering review, not an authorising signature.
  • Credentials, personal data, customer IP and export-controlled information follow declared handling rules.
  • Tool and model changes are assessed where they can alter engineering output or evidence.

Review and acceptance

Six gates from authority to handover

Gate depth changes with the work; named decisions and evidence remain.

  1. G0
    Scope authorityOutcome, boundaries, roles, assumptions and commercial basis agreed.
  2. G1
    Baseline acceptedKnown installed/source state, dependencies, constraints and unknowns recorded.
  3. G2
    Design approvedRequirements, interfaces, risks and acceptance evidence ready for implementation.
  4. G3
    Release candidateBuild reviewed, verified, versioned and accompanied by defect disposition.
  5. G4
    Site acceptanceCommissioning evidence, deviations and release decision recorded.
  6. G5
    Handover acceptedSource, configuration, records, recovery and residual risks transferred.

Read the complete delivery method

Assurance option

Compliance is a declared scope—not a badge

In validated or safety-relevant work, AI assistance is classified by intended use and impact. Approved requirements, source, review, verification and release records remain the evidence; tool use does not replace competent approval.

Review validation services

Relevant engineering evidence

Controlled AI-assisted change workflow

A demonstrator connecting a bounded change request to generated proposals, peer review, static checks, tests, traceability and human release.

Review this evidence

Procurement FAQ

Questions to resolve before quotation

Do you allow AI to modify PLC code?

Only inside a controlled engineering workflow with source control, competent review, verification and explicit release authority appropriate to the risk.

Can private models be used?

Yes where the deployment, model capability, support and security position suit the use case. Private hosting does not remove governance needs.

How is productivity assessed?

Against a defined baseline including completion, review effort, defects, rework and evidence quality—not generated line count.

Next decision

Describe the outcome, installed baseline and constraints you already know.

Use “Not known” where evidence is missing. The first review will separate facts, assumptions and discovery work.

Scope AI-assisted engineering software development