Virtual care infrastructure for skilled nursing
Bedside support for the hours skilled nursing teams are stretched thinnest.
Sevah gives SNFs a bedside companion that helps between rounds, drafts nurse-reviewed documentation, and surfaces the exceptions staff actually need to act on.
Built for skilled nursing workflows, with nurse review preserved before anything reaches the record.
See the bedside workflow with our team.
The public browser sandbox is offline. We now run a scoped walkthrough for operators, nurses, and facility leaders.
Why now
Post-acute care has a coverage gap, not a feature gap.
Skilled nursing facilities are being asked to manage higher-acuity residents with tighter staffing, more family expectations, and more pressure on documentation, reimbursement, and readmission visibility. The riskiest hours are still the ones between rounds, overnight, and across shift changes.
Workflow fit
Bedside support matters when it helps the next workflow.
Companion stays in the room, captures structured observations between rounds, drafts notes for nurse review, and routes only the exceptions staff need to follow up on. That gives a pilot a defined workflow to measure instead of another standalone device.
Between-round workflow
Fits the care loops SNF teams already run
- Between rounds, overnight, and across shift handoff
- Designed for thin-hours coverage, not all-day replacement
- Starts where the room is easiest to lose sight of
Structured observations
Turns room activity into reviewable input
- Turns room-level interactions into timestamped workflow input
- Gives documentation drafts more context before staff sign
- Helps the next shift start with a clearer picture
Pilot scope
One wing, one workflow, measurable rollout
- Starts with one wing and one high-pain workflow
- Expands after operational signal is proven
- Avoids a facility-wide change on day one
Clinical boundaries
Keeps review and trust in the workflow
- One wing before wider rollout
- Tight workflow scope before expansion
- Evidence-backed claims only
Product experiences
Start with Companion, then add adjacent workflows on the same stack.
Sevah does four things in sequence: check in, structure what happened, route only what matters, and help the next shift start with better context.
Today
Companion
A bedside presence that checks in between rounds, captures room-level context, and gives staff another point of contact during the thinnest coverage hours.
Where it lives
Lives in the resident room and starts with a bounded one-wing deployment.
What it enables next
Creates the room-level signal that supports adjacent workflow decisions later.
Product forms
Core motions
- Resident conversation
- Staff handoff support
- Nurse-reviewable draft notes
Operator value
Operators get room signal, nurse-reviewable draft notes, and fewer blind spots overnight.
Facility value
Facilities get a resident-facing experience that makes the building feel more responsive without starting with a full rebuild of care operations.
Next workflow
Orchestrator
The operating layer above the room: deployment controls, exception routing, fleet visibility, and rollout discipline across wings and buildings.
Where it lives
Lives with the operator, not the resident, as the control plane across units, wings, and buildings.
What it enables next
Turns a working pilot into a repeatable operating system instead of a one-off deployment.
Product forms
Core motions
- Exception routing
- Pilot controls
- Cross-building performance visibility
Operator value
Operators get a cleaner way to standardize pilots, see where workflow is breaking, and expand only when one building is actually working.
Facility value
Regional teams get visibility into which buildings are adopting the workflow well and where service quality is improving.
Longer-term workflow
Pick & Place
A service-robot experience for repetitive movement tasks such as supply runs, delivery handoffs, and low-complexity floor logistics that pull staff away from residents.
Where it lives
Lives in the hallways and back-of-house workflow where repetitive movement is stealing time from care.
What it enables next
Extends the stack from room intelligence into physical labor leverage inside the building.
Product forms
Core motions
- Supply runs
- Delivery routes
- Non-clinical floor transport
Operator value
Operators get labor leverage on non-clinical movement work so licensed staff spend more time on care, not hallway transport.
Facility value
Facilities get a clearer path from software-led room intelligence to measurable building-level operating efficiency.
Business outcomes
Buy Sevah on labor leverage and avoided downstream cost.
The case for Sevah is operational before it is aspirational. Facilities buy it to extend coverage during thinly staffed hours, reduce charting completed from memory, respond faster to changes in condition, and give families a more reliable front door without adding the same headcount burden.
Extend overnight and between-round coverage
Reduce manual documentation burden
Surface changes earlier so staff can intervene sooner
Improve family responsiveness without another full-time call layer
Team and credibility
Built for the room most software still ignores.
Sevah's credibility comes from staying narrow: skilled nursing, bedside support, and workflows that reduce blind spots instead of adding more software noise. Buyers should be able to see visible engineering work, clear staff-review boundaries, and product decisions shaped by operator pain.
“Sevah is not pitching generic robotics theater. It is building bedside support for the hardest-to-staff hours in skilled nursing, with draft notes and exception routing staff can review.”
Proof and trust
Early deployment signals beat generic promise copy.
Sevah should be judged on credible workflow boundaries and real deployment posture, not inflated claims. The company story is strongest when it stays brutally specific about where the product fits and what is ready today.
Built for skilled nursing operations
Designed to fit existing SNF workflows
Nurse review stays in the loop
Pilot-ready deployment, not broad clinical claim inflation
After the pilot
Start with one SNF workflow. Add adjacent workflows later.
The homepage story starts with overnight coverage, documentation flow, and exception routing. If a pilot proves useful, the same bedside system can later support shift handoffs, facility-enabled family updates, and a wider rollout inside the building.
01
Current pilot scope
Start where pain is sharpest and workflows are already breaking between rounds, overnight, and across shift changes.
02
Adjacent workflows
Add shift handoffs and facility-enabled family updates after the core bedside workflow is working.
03
Wider facility rollout
Roll the same reviewable workflow into more rooms and shifts once operators trust the signal and the staffing fit.
Pilot fit
See whether Sevah fits one wing before you change the whole building.
Start with a tightly scoped SNF pilot focused on overnight coverage, documentation flow, and exception routing. Measure the operational signal before broader rollout.
No resident-identifying data, unreviewed clinical claims, or inflated outcome numbers are used in this call-to-action path.
Try it live
CareOS Virtual Round Demo
Talk to Companion in real time and see a simulated documentation workflow built from the conversation.
Requires microphone access to talk to Companion
Open full screenField notes
Notes from building bedside workflows.
Deeper product and engineering context lives here, below the core homepage thesis.
Streaming transcription is faster, wrong, and confidently so.
Whisper-family models were built to transcribe whole utterances. Force them to stream and you trade away the future context they need to be accurate. At the bedside, where the last word often changes the first, that tradeoff is sharper than it looks.
Eldercare is the first ring.
The features that break autonomous delivery — slow, structured, low manipulation, high human-context — are the features that make care a tractable first deployment.
Small talk is the hardest thing we build, because there's no slot to fill.
Task bots have a finish line — book the flight, set the timer. Open-ended companionship has no success signal and nothing to complete, which is exactly why it's so much harder to get right.