40,000+ monthly alerts dialed by hand. Patient volume doubled yearly; median wait hit 1 hour 48 minutes. Agents now call in 3.5 minutes.
A chronic disease management platform monitoring more than 100,000 active patients across 20+ health systems, with its patient base doubling annually and 40,000+ out-of-range vital alerts requiring outbound response each month.
When a patient logged an out-of-range vital, a touchtone IVR and manually-dialed outbound calls meant the median time to reach them was 1 hour and 48...
“The voice was really natural and low latency, which is incredibly important. No one wants to be talking to a robot. When a patient has an important health need or question, you want that conversation to feel as natural and responsive as possible.”
Clinical AI platform for chronic disease management and remote patient monitoring.
AI voice synthesis platform for text-to-speech, dubbing, and voice cloning.
Cadence's Patient alert calls is part of this use case:
Related implementations across industries and use cases
Email moved some customers; calls moved more—but not at 350K accounts. An AI voice agent ran the calls: 33% answered, 3%+ activated.
Manual coordination slowed care. Voice agents now manage intake and housing aid via phone, scaling safely after 3,000+ safety simulations.
Rigid phone trees bottlenecked support. Now, an AI agent resolves inquiries, sums balances across systems, and routes to human staff.
Manual coordination slowed care. Voice agents now manage intake and housing aid via phone, scaling safely after 3,000+ safety simulations.
Launching one outreach campaign meant clicking through four interfaces. Now customers state the goal; Apollo's assistant runs the full loop.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
40,000+ monthly alerts dialed by hand. Patient volume doubled yearly; median wait hit 1 hour 48 minutes. Agents now call in 3.5 minutes.
A chronic disease management platform monitoring more than 100,000 active patients across 20+ health systems, with its patient base doubling annually and 40,000+ out-of-range vital alerts requiring outbound response each month.
When a patient logged an out-of-range vital, a touchtone IVR and manually-dialed outbound calls meant the median time to reach them was 1 hour and 48...
“The voice was really natural and low latency, which is incredibly important. No one wants to be talking to a robot. When a patient has an important health need or question, you want that conversation to feel as natural and responsive as possible.”
Clinical AI platform for chronic disease management and remote patient monitoring.
AI voice synthesis platform for text-to-speech, dubbing, and voice cloning.
Cadence's Patient alert calls is part of this use case:
Related implementations across industries and use cases
Email moved some customers; calls moved more—but not at 350K accounts. An AI voice agent ran the calls: 33% answered, 3%+ activated.
Manual coordination slowed care. Voice agents now manage intake and housing aid via phone, scaling safely after 3,000+ safety simulations.
Rigid phone trees bottlenecked support. Now, an AI agent resolves inquiries, sums balances across systems, and routes to human staff.
Manual coordination slowed care. Voice agents now manage intake and housing aid via phone, scaling safely after 3,000+ safety simulations.
Launching one outreach campaign meant clicking through four interfaces. Now customers state the goal; Apollo's assistant runs the full loop.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.