Where AI helps the team.

Vibe Haus’s engineering model starts with AI as part of the production process. Engineers can use it to explore unfamiliar code, propose implementation approaches, produce candidate changes, draft tests, and maintain documentation. The lead and designer bring product and experience context into that same process.

The important work is connecting these steps. An agent needs relevant context and a bounded task. A candidate change needs checks and a reviewer. A useful result needs to satisfy the original product requirement. Buying access to a model does not establish any of those things by itself.

The workflow from request to release

We shape the workflow around the codebase and the work it needs. Straightforward tasks can use a simple assisted process. Repeated work may justify automation. Open-ended or high-risk changes need more human investigation and control. The workflow should remain understandable to the people responsible for the product.

  • Define: agree the objective, relevant context, constraints, and acceptance criteria.
  • Prepare: choose the task boundaries, tool access, environment, and checks.
  • Build: use engineers and agents for suitable implementation, testing, and documentation work.
  • Verify: inspect behavior, code quality, design, test results, and failed runs.
  • Release: follow the agreed approval, deployment, and recovery process.
  • Learn: record what worked, where intervention was needed, and which improvement is worth trying.

Automating repeatable engineering tasks

By “software factory,” we mean a repeatable process: define a task, give an agent the relevant code and instructions, check its changes, and have an engineer review the result. We aim to automate more of that process where repeated use shows it works reliably.

There are three distinct things to scope: the product software, the team’s internal automation, and any workflow platform delivered to you. A product-development engagement does not automatically include a separate automation platform. If you want one, we define its ownership, operating costs, maintenance, and handover as a deliverable.

Code review, testing, and data access

Generated code is a candidate implementation. It still needs to work with the existing product, handle meaningful edge cases, and be maintainable by the people who will own it. Reviews should examine those properties instead of treating passing output from a tool as an acceptance decision.

Tool choice also affects data access and cost. Before connecting an agent to a repository or service, agree which tools are approved, what data they can use, what permissions they have, and who can authorize consequential actions. Apply the client’s requirements to the actual workflow.

How we evaluate AI-assisted delivery

The value of AI should show up in useful, accepted delivery. Measure the time to acceptance, review effort, rework, defect behavior, and total costs for a defined class of work. Include setup, failed runs, and human intervention. More generated code or more agent runs does not establish more product value.

We do not publish a fixed speed or headcount multiplier. The opportunity is to improve the system around a capable team and evaluate what that improvement actually produces in your context. Weekly R&D creates a deliberate place to test the next change to that system.