Use cases

By the end of this page you will have seen complete systems built on qRaptor end to end — what gets generated, how agents work inside the application, and how far the platform goes without an engineering team.

Complete business systems

These are full walkthroughs: the data model, the application, the agents that work inside it, and the governance around all of it.

Use case 01 — a supply chain system: customer orders, inventory and the agents that run against them.

A supply chain system is a good first example because it is unambiguously a business system rather than an assistant: relational data for orders and inventory, screens for the people who work them, APIs, roles, and agents that act on records under the same governance as a human user.

Use case 02 — an AIOps system: incidents, triage and remediation, with agents doing the predictable work.

The AIOps walkthrough shows the other shape: an operational system where most of the value is in what the agents do, but the application, its data and its approval paths still have to exist for the agents to be useful or safe.

Built without an engineering team

The platform is also usable by people who are not engineers. These three are real, working applications built by 15-year-old students — useful if you want to judge how much the platform does for you rather than how much a specialist can coax out of it.

The students, the brief, and what they shipped.

Guardian AI — built end to end by students.

Libra AI — a library management system: catalogue, members, lending.

ℹ️

These are demonstrations of what the platform produces, not customer case studies. For what a specific deployment looks like in your industry, talk to us .

Where to go from here

What is qRaptor? — what an AI system means here, and what gets generated.
Apps — how application generation actually works.
Agentic Workspace — the agents that work inside those applications.