27 specialized agents. 8 departments. One governing orchestrator. Not a pilot, not a demo — production systems running every day, with humans approving what matters.
A busy full-service restaurant group ran on scattered systems: reviews answered late or never, reservation follow-ups manual, customer data split across POS, reservation platform, and inboxes, marketing content produced ad hoc, and management reporting assembled by hand. Every gap was either lost revenue — unanswered callers and reviews — or lost hours of manual assembly work.
A master orchestrator — air-traffic control for AI agents — routes every event (new review, reservation change, content deadline, performance alert) to the right specialist agent, enforces human-approval policies, and logs every action with its cost:
Over 80% of events route through a zero-token rules engine; AI models fire only on the ambiguous cases that need them. Frugal by design, not by accident.
We measure before we claim. Metrics below are being verified against instrumented baselines and will be published as they're confirmed — the same discipline we bring to every audit.
| Metric | Baseline | After | Method |
|---|---|---|---|
| Review response time | verification pending | pending | platform timestamps |
| Reviews answered % | verification pending | pending | platform export |
| Staff hours saved / week | verification pending | pending | task-time sampling |
| Agents in production | — | 27 | n8n inventory |
Voice and missed-call recovery expansion, deeper revenue intelligence, and catering pipeline automation — added one at a time, only as ROI stays visible. That's the Expand step of our process, and the CHANGE Model governs every rollout.
Yours starts smaller — one leak, one system, measured.
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