Vibe Coding for Software Teams

AI can now build a small working app from one prompt. A real product needs AI built into how the team works at every step: requirements, design, code, tests, release, support and reading what customers say. Vibe coding means describing what you want and letting AI write the code. Vibe coding for software teams, sometimes called AI engineering enablement, adds the process around it. We set that process up with your team, run it on one real feature, and then support it.

It is for two kinds of company: one with its own IT team, and a development company that builds software for clients. Your developers stay in charge of the code and merge every change. If you want AI used well by all your staff, outside the software team, start with our AI consulting assessment.

Two risks once your whole team uses AI

Wrong output that nobody catches. AI writes plausible code — and it can just as easily write a wrong requirement, or a test that passes even though the code is wrong. An agent can also be steered by instructions hidden in a ticket, log or customer message it reads. In the process we set up, the written requirements and their acceptance criteria (the conditions the result must meet) come first, the tests are written and reviewed before the code, the AI gets only the access a task needs, and a person approves every change before it goes in, so an instruction hidden in what it reads cannot push a change through on its own.

Code, passwords and data leaving the company. What an AI tool reads may be stored or used under its vendor's terms, which we check for each tool. We decide with you where each kind of code and data may go, keep passwords and keys out of what the AI reads, and mask customer data in test data, logs and tickets before an AI reads them. The full list of risks, including what an AI agent may install or run, is in the rules we write with you.

From requirements to what customers say

In each of the seven stages below, AI drafts and a named person owns the decision.

What customers say becomes the next set of requirements, and the loop starts again.

The evidence explains why the checks matter. DORA's 2025 survey of about 5,000 technology professionals found that teams using more AI tend to ship changes faster — and to have less stable releases. Teams with automated tests, a disciplined history of every code change and quick feedback on each change benefit; without those controls, more change brings more breakage (DORA 2025 report).

Where your code may go

There are four places your team's code and data may go, and a company often needs more than one: a foreign cloud service; a cloud service in your own jurisdiction; an open tool with a model hosted in your jurisdiction; or your own server. In the sizing spec we work out with you where each kind of code and data may go, including your clients' code under NDA. You buy the tools from their vendors or from our partners.

What it costs to run

Seat plans make the bill predictable up to a usage limit; above it you either wait for the allowance to refresh or pay for extra usage. Pay-as-you-go plans bill by usage, mostly by how much code the AI reads for each task (seat and usage terms: vendor pricing pages, October 2026). Running models on your own server costs more per developer unless the team is large, and the open models you can run there usually trail the best cloud models — it is the route when code may not leave the company.

Three things drive the monthly bill: the pricing model, where your code may go, and how heavily each role uses AI (in our estimate, developers most, then QA, then analysts and project managers). For an AI coding agent, one vendor puts the average cost at about $150–250 per developer per month in large companies; heavy agent users spend more (October 2026). The larger cost is not on the tool bill: it is your people's time to review what the AI produces.

Three ways to set up the tools for a team:

• Minimal: one team (two or three developers plus an analyst or QA) on one repository, with approved tools and the written rules.
• Optimal: the whole product team by role, with a monthly note on cost and quality.
• Closed setup, for code that may not leave the company or the country: your own server, or a setup hosted in your jurisdiction. It is quoted in the sizing spec and priced separately from the pilot.

We build the cost estimate from your team's own figures, not from ours.

What stays with you: the repository and its history, the rules, the process documents, the tests and the tool accounts, all in your name.

What we don't do: resell tools or licences; work around a vendor's restrictions on which countries it serves; promise a speed or cost multiple; give legal clearance; run courses for individuals; build your product for you.

How we build our own products

We run this process as one developer working with AI agents, on one foreign cloud coding tool.

On our own software, each new feature starts as a change request with acceptance criteria, written by an AI agent in the CTO role; a person decides what is built and rules on the open questions in it. Before building, another AI checks the request against the existing code. A coding agent writes the tests first, then the code. A review agent and the CTO role check it, and a person releases it and runs the acceptance checks. In your team, a developer's review before merge replaces our review agent as the main check; the agent becomes the second one. Our chatbot platform and our insurance sales bot were built this way, across separate repositories that hand work to each other in writing.

If you run a development company

The process works role by role, and your people keep every decision:

• Developers: the AI writes code task by task from the requirements, test-first; your developers review and merge every change.
• QA: the AI generates tests from the acceptance criteria; your QA reviews them like code.
• Analysts and product managers: the AI turns interviews and tickets into requirements and groups customer feedback; the analyst signs off.
• Project managers: the AI drafts status and release notes from the work itself.
• Support and DevOps: the AI sorts bug reports, reads logs and drafts fixes, which go back through the process.

The rules we write with you sort your clients' code by sensitivity, so code under NDA goes only where that client allows. The practices we bring are change requests with acceptance criteria, a review of each request against the existing code before any coding starts, and test-first development. You roll the process out team by team, starting with the pilot team.

This page is about AI inside your software team. If you need AI that does a business task, such as answering customers or running a workflow, see our AI agents service.