Live demo · LLM integration pattern

Ticket triage with human approval

Structured output, a confidence gate and an audit log around an AI step.

Interactive demo on sample data. Runs in your browser. Not a client project. All live demos

Incoming ticket

A rule-based classifier stands in for a language model here. Nothing you type is sent anywhere.

Audit log

Decisions you make on the right appear here, the way a client would audit them.

Structured output

Classify a ticket to see the JSON an AI step would return, the confidence gate, and the approval step.

What runs in this demo

A rule-based classifier stands in for the language model. It returns the same kind of structured JSON a model call would.
A confidence gate: low-confidence, urgent and security cases are routed to a person instead of being auto-approved.
Approve, edit and reject actions, with every decision written to a visible audit log.
No model or external service is called, and nothing you type leaves your browser.

What a partner project adds

A language model behind the same JSON contract, with prompts stored in the repository and tested against labelled tickets.
A provider setting so the partner is not locked in, plus usage and cost logging.
Delivery as a reviewable pull request into the partner's codebase, with tests and an evaluation script.

Built for: Agencies and product studios adding an AI feature to an existing app.

Try another demo

Ask a question, get the answer with the passage it came from, or a refusal.

A forecast with a prediction band, tested against simple baselines it has to beat.

Paste a client brief and see which parts are AI work and what to clarify first.

Share a short brief. We’ll tell you whether the work is a fit and how a collaboration could be scoped.