Apply AI where it helps

AI helps write the code, your team decides

Task, rules, and checks are agreed before AI writes code. Every change arrives with its check results, and your team decides whether to accept it.

Who it's for
Development teams and technical leads who already use AI for coding and want changes they can review with confidence.
Programs
  • Task board
  • Code repository
  • AI assistant
  • Automated checks
AI
Part of the workflow
Starting point
One team, one repository, a few real tasks

The workflow at a glance · 6 steps

See it step by step
  1. Step 1 – Trigger: A task is agreed
  2. Step 2 – Automatic: Rules and context are ready
  3. Step 3 – AI: AI makes the change
  4. Step 4 – Automatic: Checks run on exactly this version
  5. Step 5 – You decide: Your team reviews and accepts
  6. Step 6 – Result: Accepted, with a record
  • How a task flows
  • You decide here
Where things stand

How it often runs today

The team has been coding with AI for a while, but everyone in their own way. What the AI needed to know sits in individual chats. In review, someone asks what was intended and re-runs the checks. It isn't always clear whether a test result belongs to the current version.

Where it gets stuck day to day

  1. Everyone works with AI differently
  2. Reviews lack the context
  3. Check results don't clearly match the change
  • Gather requirements from the ticket (between Task board and you)
  • Explain the context to the AI every time (between you and AI assistant; point 1 above)
  • Paste AI suggestions in by hand (between AI assistant and Code repository)
  • Ask in review what was intended (between Code repository and you; point 2 above)
  • Re-run checks by hand (between you and Automated checks)
  • Match up check results (between Automated checks and Code repository; point 3 above)
Today: You are the link between four programs.Where it gets stuck (see above)
The workflow

Step by step

This is how the workflow can look once it is set up: six steps, one of them yours.

Step 1 – Trigger: A task is agreed

The task board says what should change, what stays untouched, and how your team will know it's right.

  • Task board

Instead of: Gather requirements from the ticket

Step 2 – Automatic: Rules and context are ready

Each type of task comes with clear rules: what the AI may do, when a person decides, and when it stops. The AI also gets your conventions, the affected parts of the code, and the commands for the checks.

  • Code repository
  • Team rules

Instead of: Explain the context to the AI every time

Step 3 – AI: AI makes the change

A small, reviewable change is made in a separate working copy. The AI writes down assumptions and open questions instead of guessing.

  • AI assistant
  • Working copy

Instead of: Paste AI suggestions in by hand, Ask in review what was intended

Step 4 – Automatic: Checks run on exactly this version

Tests and your existing checks run against the proposed version. If something fails, the change goes back to a person with the check report.

  • Automated checks

Instead of: Re-run checks by hand

Step 5 – You decide: Your team reviews and accepts

In review, the task, change, check results, and open questions sit together. The reviewer decides whether the change is accepted.

  • Code review

Step 6 – Result: Accepted, with a record

The change goes live through your usual release route. Check results and the decision stay with it, and recurring mistakes feed back into rules and checks.

  • Code repository
  • Task board

Instead of: Match up check results

What changes

The same programs, less manual work

Your programs stay. The workflow handles the work in between, and you step in where your decision is needed.

Today: You are the link between four programs.With automation: Your programs are connected. You approve in review.

  1. Before: Everyone works with AI their own way

    With the workflow: One shared workflow with clear rules

  2. Before: Explaining the context to the AI every time

    With the workflow: Conventions and context are maintained and ready

  3. Before: Reconstructing in review what was intended

    With the workflow: Assumptions and open questions come with the change

  4. Before: Re-running checks by hand

    With the workflow: The check results belong to exactly this version

Building blocks

What the workflow is made of

Where the details come from, what connects them, and where they end up. Below: how you stay in control.

Input

  • Task board
  • Code repository

Processing

  • An AI assistant that works within your rules
  • Automated checks on every proposed version
  • OptionalSelf-correction after failed checks

Output

  • Code repository
  • Task board

Control

  • A person decides on every change in review
  • The AI cannot change acceptance criteria or checks
  • Sensitive areas only with sign-off from the person responsible

Your data

The code stays in your repository. You decide beforehand which AI services you use, which parts of the code they may see, and which areas are off limits.

How to start

Start small, then build out

We implement larger solutions in stages. Before you invest in expanding them, we test the first version in your business.

  1. One slice, up to your decision

    Prototype

    One team, one repository, and a handful of real tasks: a small prototype with shared rules.

  2. The whole workflow, in daily use

    Put it to work

    The workflow covers suitable everyday tasks. Every change goes through review.

  3. More inputs and results

    Build out

    More task types, self-correction after failed checks, and rules you sharpen based on recurring mistakes.

A task that's hard to hand off?

Bring a development task that is hard to hand to AI or hard to review. In a free intro call, we'll look at where a prototype could start.

Book a free intro call