Where care delivery ends and billing begins.
Senior living communities run on thin margins and labor-intensive workflows, with billing, scheduling, compliance, and resident data spread across systems that do not connect automatically. Coordination between those systems falls to your staff. That coordination time is what AI addresses, not the care itself.
As resident acuity rises and care staff remain difficult to hire, the administrative load grows alongside it. ERP AI Agent builds AI agents that work alongside your existing EHR, scheduling, and billing platforms to return that time to the people who should be spending it with residents.
Care level changes recorded in your EHR frequently do not reach billing in time. Finance coordinators spend 8 to 12 hours at month-end identifying unbilled services and correcting charge errors before statements go out.
When a caregiver calls out, the scheduling coordinator manually contacts the availability list one contact at a time. A single call-out can consume 30 to 60 minutes of coordination time, and most communities manage several per week.
Incident reports, care notes, and state-required documentation are entered manually across multiple systems. Directors spend significant time tracking missing entries before surveys and audits, not reviewing outcomes.
Sales counselors manually track inquiry lists and follow up inconsistently when occupancy demands compete for their attention. Prospective residents and families move on when first response is slow.
| Good Fit | Not a Good Fit |
|---|---|
| You operate one or more communities with stable billing, scheduling, and compliance workflows already in place. | You are mid-implementation of a new EHR or financial system with no stable operational baseline yet. |
| You have a care or property management platform and want AI to work alongside it, not replace it. | You expect full automation with no human oversight of decisions affecting residents or staff. |
| You have recurring, high-volume processes where staff coordination time is disproportionate to the task complexity. | Your recurring exception volume is very low, fewer than 20 incidents or billing variances per month. |
| You are ready to pilot one process before committing to a broader deployment. | You are looking for AI to resolve an occupancy problem that originates in pricing, product fit, or market positioning. |
Before: A billing coordinator spends 8 to 12 hours at month-end finding unbilled services across care level changes, ancillary charges, and service upgrades entered in the EHR but not yet invoiced.
After: The agent surfaces variances daily as care records change. Month-end review takes under 2 hours, and unbilled revenue is recovered within days, not at statement time.
Before: Each call-out requires 30 to 60 minutes of manual outreach. Weekend and overnight call-outs fall to on-call managers working through the same manual process.
After: The coverage agent contacts qualified staff within minutes of a call-out and fills most shifts without coordinator involvement. Human decision is required only when no qualified coverage exists.
Figures shown are representative of outcomes in comparable implementations.
Representative screens showing how AI agents surface data and present decisions to your staff. Click any card to see the full view.
Operations Dashboard
Billing variances, census alerts, and AR aging surfaced in one view for your finance coordinator.
Revenue Leakage Detection
Care level changes that have not yet reached billing, flagged daily before month-end close.
Voice AI Shift Coverage
Call-out coverage agent working the availability list, logging responses, and filling the shift.
We work with your operations, billing, and care management leads to select one high-volume process, confirm data access in your care and finance platforms, and agree on measurable success criteria before any build begins.
One process, one agent, your real environment and real data. Typical pilots cover billing reconciliation, shift call-out coverage, or inquiry follow-up. Human review is built in at every decision point that needs it, and results are measured against your baseline.
When the pilot meets its success criteria, the same integration foundation supports additional agents and processes. You decide what comes next based on measured outcomes.
Yes, in most cases. We build agents that read from and write to your existing platforms through API connections or structured data outputs. Your EHR is not replaced or modified.
Yes. Billing and AR agents connect to the financial platform you already run, including SAP, Acumatica, and other established systems, alongside your care platform. Neither system is replaced or modified.
A single-process pilot runs 6 to 8 weeks from kickoff to live operation. Most clients see measurable time savings within the first 30 days of the agent running in production.
Only when the process requires it. Billing and scheduling agents typically work with operational data only. Any access to clinical records is scoped and disclosed during pilot scoping, before any build begins.
The agent escalates to a human with full context attached. Every agent we build includes defined escalation thresholds. Staff are never removed from decisions that require judgment.
Compliance obligations depend on how the agent processes and stores data. We flag every HIPAA consideration during pilot scoping and build within your existing security and permissions model wherever possible.
Cost depends on process complexity and integration requirements, and is fixed before any work begins. That investment covers one process, one agent, and the scoping work that precedes it.
Your team participates in 2 to 3 structured sessions during the implementation period. Ongoing oversight after go-live is typically 2 to 4 hours per month per deployed agent.
Book a thirty-minute call. We will confirm whether your situation is a fit and what the right starting point is, whether that is the AI Adoption Accelerator, a readiness assessment, or a direct pilot.