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AI in the development workflow. Engineering judgment throughout.

Help developers use AI deliberately, with practical tooling, repeatable workflows, and clear expectations for quality, security, and review.

Talk about your development workflow
  1. 01

    Workflow and tooling review

    Identify useful roles for AI across research, implementation, debugging, testing, and review, alongside the team's existing tools.

  2. 02

    Practical enablement

    Work through representative engineering tasks. Practice providing context, breaking down work, inspecting changes, and recovering from poor output.

  3. 03

    Repository guidance

    Establish project instructions, conventions, and useful context that make the team's expectations explicit.

  4. 04

    Quality gates

    Define what needs automated checks, tests, code review, and a responsible human decision before merging or release.

  5. 05

    Security and data boundaries

    Clarify expectations for secrets, proprietary code, tool permissions, dependencies, and generated changes.

  6. 06

    Pilot and iteration

    Try the workflow on a bounded area, capture what helps or fails, and improve the team's practices.

What you can
take forward.

Depending on the agreed scope, the work can include:

  • Development workflow assessment
  • Tooling recommendations
  • Hands-on enablement sessions
  • Repository guidance and team playbook
  • Review and testing expectations
  • Pilot and follow-up plan

Bring the problem.
We’ll shape the path.

Share the stack, delivery process, tools already in use, and the problems the team wants to solve. A bounded pilot is a useful way to build a repeatable practice.

What are you
working towards?

Tell us what you’d like to build, improve, or bring into your organization. A few details are a good place to start.

info@parrisdigital.com

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