Summary

SLAtech LTD is an Israeli software development company, operating since 2004. We help teams and product owners whose code was written with GitHub Copilot: we check the architecture, security and tests, the repository rules and how the agent's changes are reviewed, take the project to production and keep developing it. The code stays in your repository. People define the architecture and make the decisions; AI only proposes.

GitHub Copilot projects

Your GitHub Copilot project is stuck? We will check it, fix it and take it to production

GitHub Copilot is built into where the team already works: it suggests code in the editor, carries out tasks in agent mode, and the coding agent picks up an issue from GitHub on its own and opens a draft pull request. The trouble starts when there is more Copilot code than the team can read carefully. Suggestions are accepted without a look, tasks for the agent are written in two lines, and reviewing the changes is left to Copilot as well. We audit GitHub Copilot projects, continue their development, or rewrite what is not worth fixing. What we fixed, we stay accountable for.

Tell us about the project Go straight to the audit

What GitHub Copilot does well

Works inside GitHub and the editor. Issues, branches, pull requests and checks stay where they were.

An agent for tasks. You can assign an issue to Copilot, and it comes back with a draft pull request on a separate branch.

Repository rules apply to it too. The agent's changes go through the same checks and approvals as people's.

Automated code review. Copilot can leave comments on a pull request.

That is why code written with Copilot usually does not need to be thrown away. More often what needs checking is how carefully it was accepted.

Where GitHub Copilot projects usually get stuck

Suggestions accepted without a look

Line by line everything looks right, while together it adds up to code nobody designed.

A two-line task

The agent does exactly what the issue says. If it has no constraints or criteria, the result is formally correct but wrong in substance.

Copilot reviewing Copilot

Automated pull request review is useful, but it can miss the same things the agent missed. Responsibility for a change still lies with a person.

Tests by the same author

If the tests are written by the same agent as the code, they check what it did, not what was intended.

Branch protection not everywhere

If approval is not required, the agent's changes can reach the main branch without human review.

Unneeded and made-up dependencies

The agent adds libraries for each task, sometimes outdated ones or ones with names that look like real packages.

What we check in a GitHub Copilot project

1

Repository rules: branch protection, required checks and approvals.

2

Review procedure: who reads the agent's changes before merging, and how.

3

Architecture: where each piece of logic lives and where it is repeated.

4

Application security: access rights, input validation.

5

Tests: what they actually check and which main scenarios are not covered.

6

Dependencies: unneeded, outdated and vulnerable libraries.

7

Keys and secrets: in the code, in the change history, in GitHub Actions settings.

8

Database work: queries, indexes, migrations.

9

Deployment: branches, checks before merging, a separate environment for checking.

The result: a written report in plain language, a list of issues ranked by risk, and a plan of fixes.

Three ways forward

Audit

A check against the list above, a written report and a plan of fixes. A fit if you carry on yourself or with your own team.

More about the audit

Continued development

We join your team or run the product ourselves: we close the critical issues and keep developing it. What we fixed and wrote, we are accountable for.

Discuss continuing

Rewriting a part

Usually not everything needs rewriting, only the one area where the logic is most scattered: access rights, payments, data handling.

Discuss a rewrite

How we choose between these paths is described in detail on the page about helping vibe-coded projects.

How we use AI ourselves

GitHub Copilot suggests code and can carry out a task and open a pull request on its own. We work differently. On client projects, people define the architecture and make the decisions. AI proposes a solution to a task a person has set, and we bring it into the project ourselves, after review. The AI agent gets no access to the code, databases, keys or passwords, and there is not a single line in the code that a person does not know about.

Frequently asked questions

Automated review catches a lot within a single change, but it does not see the system as a whole: repeated logic, outdated decisions, gaps in access rights. That is a job for a person who understands the product.

As for a new developer: what to do, what not to touch and how to check the result. The more precise the task, the less rework.

No. We are not against AI tools. What matters is that tests, human review of changes and a clear system design sit alongside them. The audit shows where these are missing.

Read access to the repository. We sign an NDA if needed. Access to the database and production is discussed separately, and only when it is really needed.

It depends on two things: the size of the project and how far it has drifted, that is, how much in it needs changing and fixing. After the initial assessment we name the scope of work and the price, before any work starts.

Other AI tools

We work the same way with projects built on other tools. The general page: help for vibe-coded projects.

Tell us what you built with GitHub Copilot

Describe the product and what is holding it back. We will take a look and suggest where to start: an audit, continued development or rewriting a part.

Tell us about the project Go straight to the audit