Qubitec provides
- Engineering with AI agents and human oversight.
- Specifications, implementation and verification.
- A portal for progress, history and evidence.
A QubiPod combines autonomous AI agents with Qubitec engineers to build and evolve enterprise software. Beyond prompting: agreed specifications, human oversight, and verifiable evidence at every delivery.

The model generates or analyzes. The agent executes a role with tools and limits. A named person approves what reaches production.
Your team makes the business decisions. Qubitec handles the engineering and keeps progress visible.
Describe the problem, the expected outcome and the systems involved. Together, we define the initial scope.
A request with an objective and initial scope.
Review the proposed solution and acceptance criteria. Approve the definition or request adjustments.
An agreed definition before implementation.
Engineers and agents work from specifications. Check the status, next action and available evidence in the portal.
Linked code changes, checks and history.
Verify the outcome against the agreed criteria and confirm delivery acceptance through the portal.
An accepted delivery and its history.
Buying hours does not guarantee outcomes. Generating code fast does not guarantee operable software.
Each QubiPod works within a business domain and its systems, with agreed priorities and context specific to your organization.
Agentic development only scales when bounded by clear limits, human reviews, and automated gates before any code is written.
No code is built without an agreed proposal, technical specifications, and clear acceptance criteria.
Formal validation of dependencies, tech stack, and access policies before build authorization.
Named engineers review every change. Commit history and automated test logs are always verifiable in your portal.
All software lives in your infrastructure and repositories. No models trained on your secrets, no unauthorized retention.
Metrics are built from runs, tasks, controls and recorded acceptances — not from progress reports.
Equivalent hours and cost comparisons are estimates based on assumptions. They are a reference, not proven savings or a guaranteed return.
Adoption already happened. Trust did not follow — and more AI does not automatically mean better delivery.
Sources: Stack Overflow, 2025 Developer Survey — AI. DORA, Impact of Generative AI in Software Development & Accelerate State of DevOps Report 2024. DORA values are reported associations for a 25% increase in AI adoption.

Your team registers needs (FLR) and watches them move through the six ADD Cycle stages — with the next action always explicit.

Each pod shows its domain, coverage and current work — and the accepted deliveries of the period, not promises.
Email us to request a demonstration. Tell us what you need to build or improve and which systems are involved.
You can also copy the address manually.