Reference systems with visible control boundaries.

These code-native previews demonstrate how Draya approaches retrieval, evaluation, scoped tools and human handoff. They are reference implementations—not client work or outcome claims.

Concrete system behaviour, presented without fabricated proof.

Each preview explains the operating problem, the system path, the boundary that keeps a person in control and what can be demonstrated in a focused conversation.

AI Chat Agents

Reference implementation — not a client case study.

Chat-agent controlled-resolution workflow

Operating problem: A support case needs current account context, approved knowledge and a clear escalation boundary.
Implemented system: A staged resolution path classifies risk, retrieves evidence and checks confidence before responding.
Control boundary: Sensitive actions and uncertain answers move to a human with the assembled context.
What can be demonstrated: Ticket intake, evidence retrieval, confidence decisions and handoff state.

Knowledge systems

Reference implementation — not a client case study.

RAG evaluation and observability console

Operating problem: A grounded answer is only useful when retrieval quality and supporting evidence can be inspected.
Implemented system: A reference console pairs retrieved sources with evaluation checks and observable response state.
Control boundary: Low evidence coverage is marked for review instead of being presented as verified output.
What can be demonstrated: Source inspection, retrieval checks, evidence coverage and review state.

AI Product Studio

Reference implementation — not a client case study.

AI agent workflow product prototype

Operating problem: A useful agent must coordinate a real workflow without receiving unrestricted system access.
Implemented system: A focused product surface connects one user objective to scoped tools, validation and an observable result.
Control boundary: Tool permissions are explicit, with approval before consequential actions.
What can be demonstrated: Task definition, tool selection, approval checkpoints and deployment-ready output.

Map one of these system patterns to a real operating problem.

Start with the workflow, data, risk boundary and measurable outcome—not a generic AI feature list.

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