A Québec health network needed to know whether AI could read clinical urgency reliably — in Québec French as readily as in English — before committing to a build. Ngenium engineers answered that question on fully synthetic voicemails, with no patient information involved.
Phase-0 proof of concept · synthetic data · no patient informationThis was a Phase-0 proof of concept: a go/no-go evaluation built and scored on a purpose-built dataset of synthetic bilingual voicemails. No real patient left any of these messages, no protected health information was processed, and no part of this ran against a live clinical queue.
It demonstrates the solution Ngenium proposes to deliver. It is not a completed production deployment and not a delivered customer outcome. Every number on this page carries that label beside it, because a proof-of-concept measurement and a production result are different claims and should never look alike.
When call volume peaks, patients roll to voicemail — and those messages get worked first-in-first-out, triaged only after a human listens to each one. A patient describing chest pain can sit behind dozens of routine scheduling requests, and nobody can see it happening.
All three came from one root cause: voicemail is opaque. Urgency is locked inside an audio file and stays invisible until a person listens — so prioritization, SLA protection and supervisor visibility are all impossible at the exact moment they matter. The evaluation demanded something that could make urgency legible the instant a message arrived, in either language, while keeping patient information inside the hospital network.
CADENCE sits behind the existing Cisco voicemail system rather than replacing it. The same code runs hosted for a demonstration or fully on-premises in production, so patient data never has to leave the hospital.
The work was structured so the riskiest assumption was tested on synthetic data before anyone committed budget to a clinical deployment. Only Phase 00 has been delivered.
In the Phase-0 evaluation, every synthetic emergency was surfaced as Critical, and the most urgent callbacks were reached far sooner than first-in-first-out — enough evidence to move forward to a clinical pilot.
Running Phase 0 in the cloud let our engineers compare hosted and open-weight models side by side, test accuracy across languages and accents, and size the Cisco AI POD and GPUs — so the customer can choose the on-premises model and hardware on evidence rather than guesswork, before committing budget.
In the proposed production design the entire pipeline runs on the on-premises Cisco AI POD, so no audio, transcript or patient information leaves the hospital data center. The proof of concept confirmed an open-weight model matches the hosted benchmark closely enough to make that realistic.
CADENCE only ranks — it never closes, dismisses or medically judges a message. Every ranking shows the words that drove it, and an agent can re-tier any message with one click. The deterministic safety net does not depend on which model is chosen.
CADENCE sits behind the existing Cisco Communications Manager and Unity Connection, adding an AI prioritization layer to voice infrastructure the network already owns, and landing the new workload on a Cisco AI POD.
No. Every figure on this page was measured on a fabricated dataset of synthetic voicemails containing no patient information and no protected health information. This was a Phase-0 proof of concept run to answer a go/no-go question. It is a demonstration of capability, not a delivered clinical outcome.
We do not name customers without explicit written permission. This proof of concept was built for a Québec health network operating a bilingual clinical call centre with patient voicemail.
Not in the proposed production design. The pipeline is built to run entirely on an on-premises Cisco AI POD, so no audio, transcript or patient information leaves the hospital data center. The proof of concept confirmed that an open-weight on-premises model matches the hosted benchmark closely enough to make that realistic.
No. CADENCE ranks messages; it never closes, dismisses or medically judges one. A deterministic rule-based safety net force-escalates emergency phrases regardless of what the model says, low-confidence calls are flagged for human review, and an agent can re-tier any message with one click. A person is always in the loop.
The demonstration is login-gated. Ask us for a walkthrough and we will show you the ranked queue, the safety net and the supervisor view.