Access to actionable data
Driven by CloudCare OS and CareSignals.ai. See the systems
Skip the waiting room.
CloudCare is the AI-driven platform, Beyond RPM, that sits above the EHRs and connects any AI platform to the enterprise you already run, with minimal disruption.
Our mission
Put working health programs in front of the people who need them in weeks, not years, on the systems they already run, with AI they can govern.
Sovereign means you own all of it: your data, your AI, your environment and the pace of change.
Convergence
AI is collapsing the delivery of care. CloudCare is the bridge to the other side.
CloudCare is the AI orchestration platform that connects the home to the care team. Built for solution integrators and the government agencies they serve: one elastic platform that compresses the middle, from the enterprise to the home.
Beyond RPM · Unstick the enterprise
Sitting above the EHRs, CloudCare is the connective tissue between your enterprise and any AI platform.
It energizes existing healthcare enterprises with the power of AI, with minimal disruption to the code stack they already run.
CloudCare · 01
A way to ask, a way to reach, a way to act, a way to understand, and a way to prove. Nobody sells that as one thing, so everybody assembles it.
CloudCare is that list as one platform: the platform a health program runs on, with or without an EHR beneath it. Where there is an EHR, it stays the system of record, and CloudCare is the system of execution for everything that happens between visits.
CloudCare · 02
The ER visit, the admission, the readmission rarely arrive without warning. CloudCare watches at scale, reaches people at home and catches the drift before it becomes a hospital bill. Three forces make it urgent now.
Chronic disease is rising as clinicians leave. Care has to go virtual-first.
More than $1.5 trillion in initiatives is moving payment from volume to outcomes.
No data, no AI. And AI strips out the middle processes that add cost and delay.
Lower-cost, scalable technology shifts care left, from reactive, high-cost complex care toward proactive health and wellness, and to a far larger virtual-first population than traditional remote monitoring can reach.
CloudCare · 03
Healthcare has no shortage of data. It has a shortage of insight. A patient’s record is spread across many systems, consolidation takes years and ends in a new silo, and care arrives late: VA remote monitoring reaches about 70,000 veterans, while the VA estimates 700,000 to 2 million could benefit.
NASA stopped building its own spacecraft and started buying crew and cargo flights from companies such as SpaceX.
Twenty medical markets consolidated into nine Defense Health Networks.
The Veterans Health Administration is being reorganized, with a new AI strategy.
CloudCare · 04
New systems added clicks and alerts while the old work stayed. Skepticism is earned.
Remove work people can feel, quickly. That trust funds the next, larger change.
Success is measured in results people feel, not in tools installed.
We didn’t build a house. We built a subdivision.
The streets, water, power and cable are laid once, underground. Every program built on CloudCare makes the next one cheaper, and the lots are surveyed and ready. The only question left is which house is yours.
CloudCare · 05
Each part answers one question. Follow them in order, or jump in anywhere.
CloudCare · 06
Integrators are our customer. Agencies are the mission they serve.
Answer a CSO or AOI with a working prototype, then field it under your own brand.
VA, DHA and state agencies get capability in weeks, on the systems they already run, and own the data, models and deployment.
The programs they deliver reach every side of care
One panel for monitoring, alerts and billing-qualifying events.
Close care gaps and lift Stars, with the reporting to prove it.
Integrate a device once and reach every program on the platform.
Tech-enabled touchpoints and conversational AI keep people connected to their care team.
Integrators are our customer in federal work. Agencies and care organizations are who they serve.
CloudCare · 07
Real patients, peer-reviewed results.
Forged in VA connected care, then hardened for enterprise and home.
Issued U.S. patents on core connected-care methods, independently valued.
Shaped by federal programs and health systems, carried to market by technology and channel partners.
“This is what we have been waiting for.”
Combining proactive monitoring with community-based intervention did not just reduce readmissions. It improved equity in how care is delivered, with support tailored to each patient’s needs.
2.6% figure: Jafri et al., The Joint Commission Journal on Quality and Patient Safety, 2024. White Plains Hospital (WPH Cares) on the Life365 virtual care platform.
CloudCare · 08
Most platforms address one or two. CloudCare sits at the intersection of all five, by design.
The five predictions are from CB Insights’ 2026 digital-health outlook. CB Insights does not endorse or name Life365. The alignment shown is Life365’s own analysis of CloudCare’s capabilities against those predictions.
Beyond RPM · The Convergence Trilogy
Two decades building toward one conviction: that healthcare would converge, at a single point in time, into one collapsed offering. The Convergence Trilogy is that vision written down. CloudCare is its execution.
V2R is Vision to Results. Clinicians set the goal, AI assembles the program, people check the result.
V2R · 01
Between a vision and working software sits a long middle of requirements, designs, tickets and code. Each handoff loses something and takes weeks. V2R removes it.
The bars are illustrative.
| Step | Integrator alone | On CloudCare |
|---|---|---|
| Working prototype | Months of specifications first | Fast, from what you already have |
| Live demo | After a long build | Early, on working software |
| Production | Slow, and rebuilt from scratch | Faster, with no rebuild |
| Security | Added at the end | Built in from day one |
| Who steers | Consultants | Clinicians |
A demo is not production, so both are shown.
V2R · 02
Four steps. Press play, or tap through.
Building a first version…
The code is an illustration of what Claude builds and the prototype environment seals, not a real listing.
V2R · 03
Set the bar, run the loop, harden on CloudCare. No one grades their own work.
Clinical and quality owners pour their domain knowledge through the AI tools they already prefer. The vision becomes concrete bars and kill criteria.
Output: the Charter and locked requirementsA three-to-five person pod runs builder and critic loops against the bars. AI critics score continuously; clinicians judge. Every capability ships as the three-mode ladder.
Output: client-validated working softwareThe winning artifact is ingested by CloudCare OS, not thrown away. Security, conformance and accreditation evidence are worked through mechanically.
Output: fielded capability and a stronger platformClaude, Copilot, OpenAI, Gemini or Cursor. AI runs free only in ideation, where end users verify what it makes.
From the second gate on, code is assembled from Knowledge Services through the MCP Connector, inside the GuardRails.
Creative AI stays in ideation. Only signed-off, ported, loop-tested work reaches production.
V2R · 04
Set the bar. Run the loop. Harden on CloudCare. Tap a tab to see how V2R works under the hood.
V2R · 05
A small joint team rebuilt a federal periodic health assessment with V2R: a working system, not a mockup, in an accreditable environment.
V2R asks every capability to carry a scored record. The same instrument is turned on the method itself, and the failures are written down beside the findings.
V2R · 06
Two lanes run side by side. The fast lane wins work through CSOs and AOIs. The long game builds SBIR-backed rights for follow-on work. In both, the integrator is prime.
Illustrative path. Phase III eligibility depends on prior SBIR Phase I or II work by the awardee, and terms vary by agency. Confirm details with counsel and the agency’s SBIR office.
V2R · 07
Construction is not coming back. Clinical and quality owners hold the rudder at every stage, and the builder never grades its own work.
Determine the clinical workflow, the bars and the guardrails, under governance. They steer every stage and check every AI output.
Put the technical pieces and modular components in place on low-code, inside Knowledge Services guardrails, exactly as the clinical workflow defines.
Run the workflow day to day. Bots act only where clinical owners grant permission.
Clinical and quality owners turn the mission, instruments and constraints into measurable bars and kill criteria.
The input AI optimizes toward, not a task AI replaces.Designing and tuning AI critic benches, curating reference standards, and escalating to clinical judges.
The governance that makes AI speed trustworthy.Turning V2R records into accreditation packages, governance submissions and audit responses.
Regulated markets pay for provable trust.Growing Knowledge Services, mapping each capability into the shared model so reuse stays high.
The compounding asset itself.V2R · 08
The enterprise does not need to become small. It needs to become many small teams on one platform, governed by portfolio instruments rather than program ceremony.
Time to first artifact, bar-pass rate and iteration velocity on one dashboard. The factory’s gauges replace status reporting.
Kill criteria fire automatically and without stigma. A pursuit stopped in week one is a success of the method.
The share of each capability inherited from CloudCare rather than newly built: the best measure that platform economics are compounding.
Charters, bars and V2R records standardized across pods, so any pursuit is legible to leadership in minutes.
CloudCare OS connects into the systems healthcare already runs, so teams build modules instead of plumbing.
CloudCare OS · 01
Five engines that reach the systems you already run. Tap one.
CloudCare OS · 02
Every era removed a scarce resource. Enterprise software moved from static code bases, to modular platforms, to CloudCare: elastic enough to absorb AI and every integrated system, and liquid enough that functionality and scale flow without a rebuild.
| 1990s · mainframe to ERP | 2020s · point solutions to CloudCare | |
|---|---|---|
| Scarce resource | COBOL programmers | Time and tokens, with precise talent |
| Fragmented stack | Database, transaction monitor and tools sold separately | RPM, EHR, devices, billing and analytics point solutions |
| Unifying platform | SAP / Oracle: one program, one data model | CloudCare OS: the care delivery operating system |
| Method | Configure modules instead of coding | V2R: set the bar, assemble and check, harden on CloudCare |
| Knowledge | Best practices built into the ERP package | Knowledge Services: prompts, rules, templates, APIs and baselines |
| Who builds | Functional consultants | Clinicians steer; Business Engineers build on low-code |
| Intelligence | Reports after the fact | Care Intelligence: continuous insight |
CloudCare OS · 03
Left alone, AI generates pieces that do not fit. CloudCare assembles from proven parts, guardrails included.
The model is a commodity. The knowledge it assembles from is not, and every module adds to it.
CloudCare OS · 04
At the center is CLOIE™, the CloudCare Learning Orchestration Interoperability Engine. It automates fulfillment, outreach, education and AI-driven conversations that personalize care in real time.
Absorbs change and self-heals. The platform adapts instead of breaking.
CLOIE refines, tags and routes every signal into action.
The right signal to the right clinician at the right moment. This is IRIS.
Every signal from every module on one screen, with one login.
Order out, results back into the record: complete data rounding.
CloudCare OS · 05
AI providers change constantly. CloudCare detects the change, repairs itself and keeps serving. Break something and watch.
Illustrative simulation with synthetic data.
CloudCare OS · 06
Raw data stays where it lives. The Refinery makes it understood, connected and yours. Try one example three ways.
Illustrative example with synthetic data. It does not describe any service’s actual systems or any agreement.
CloudCare OS · 07
Like a refinery turning one barrel of crude into many fuels, CloudCare turns patient-generated health data into a slate of refined insights. The underlying clinical data is never altered.
It reads more than the number. A reading taken means a patient engaged. A reading on time means a plan honored. A pattern means a risk worth watching.
CloudCare OS · 08
From the device in the home to the workflow in the enterprise, CloudCare is one stack. Each layer is independent and replaceable, and none is locked to one vendor.
CloudCare OS · 09
Run it where you already operate, and connect every major EHR over FHIR R4 and HL7.
Open standards throughout. HIPAA and HITECH compliant, FedRAMP-ready on any cloud.
AI platforms and data structures change constantly. CloudCare OS is built to take the change and keep serving. Pick something to break.
CloudCare OS · 10
Engineers hand-write pipelines and adapters. Every change in a source system or AI platform becomes a ticket, and the people who know the business wait in line behind it.
Business engineers state the rule and the outcome. The platform generates the plumbing, maintains it as things change, and asks a person only when it is genuinely unsure.
CloudCare OS · 11
What the platform means for a program, in five groups.
Performance statements are based on Life365 engineering data; validated measurements will be published as they become available.
CloudCare OS · 12
Three layers. The base is owned by Life365 and backed by its patents; partner capabilities are wired in once so every program inherits them; specialist vendors are chosen per program.
Partner names describe integrated capabilities, not endorsements.
CloudCare OS · 13
Six reasons it is hard to copy, even with the same AI models.
Assembled from proven parts in Knowledge Services, not generated by AI. Same AI model, still no quick copy.
Issued U.S. patents across front-end data capture (wearables, LeanOS, mobile entry, location-based services), with CloudCare provisionals filed.
Wired into partners’ identity, EHRs, cloud and infrastructure. Replacing it means re-plumbing.
AWS GovCloud, deployment boundaries enforced in code, continuous compliance self-tests. Slow and costly for new entrants to earn.
Every program adds parts to Knowledge Services and signal to Care Intelligence. A late entrant starts from zero.
Clinicians steer every step, proven on a recent federal health program. Federal references are earned, not bought.
What comes next · Gibber Code
Today software is code people read line by line. Next, AI systems exchange machine-native messages, system to system, assembling from Knowledge Services. AI agents act as Business Engineers, and as buyers that find CloudCare modules through the MCP Connector.
When no one reads the code, you govern the results.
The right systems, aimed at the one mission you define.
Care Intelligence · 01
Each mission lines up the systems that serve it, in order.
Each card opens that system’s live screen right here, shown with synthetic data. Digital Twin and Clairvoyant outputs are decision support, not actuarial or clinical determinations.
Care Intelligence · 02
Each surface answers one question and hands it to the next, so the chain ends with something done.
IRIS renders inside all of them. It says what moved, why, and what it did about it, and every line clicks through to the work, including what it declined to do.
The constraints are part of the product. These are design principles of Care Intelligence.
| The temptation | What CloudCare does instead |
|---|
Care Intelligence · 03
Healthcare has no shortage of data. It has a shortage of insight. CloudCare is the platform, V2R is the method, and Care Intelligence is the third leg: six surfaces that turn connected signal into a decision someone can act on, and then act on it.
A score tells you who is sick. A forecast tells you who to call this morning.
Every action here. Nothing in a second tab.
Illustrative, for an enrolled remote monitoring patient: two device readings a day plus voice and labs. Dragon Copilot is Microsoft’s product; CloudCare renders inside it.
The scribe owns the room. CloudCare owns the year between rooms.
Nothing edits DNA. Authority is granted by the member and can be withdrawn in one click.
Capacity follows demand, not a fixed roster. A mode of capacity, not a video visit.
An assistant with its own tab is a place someone has to remember to visit. IRIS renders inside all six surfaces: it says what moved, why, and what it did about it, and every line clicks through to the work, including the work it declined and handed back.
One teammate, six contexts. What it will not do is the same in all six.
Care Intelligence · 04
Three journeys CloudCare carries start to finish.
The 30 days after a hospital stay decide whether a patient comes back. CloudCare carries them from the discharge order into monitored recovery at home: early readings, check-ins, and escalation before a setback becomes a readmission.
The costliest events are the ones seen coming. CloudCare watches the whole panel between visits and surfaces the patient trending the wrong way, turning a rising pattern into an intervention instead of an ER visit.
What proves out at one clinic has to hold at the scale of millions. CloudCare runs the same program from a single site to a health system or federal population, reaching even patients with no smartphone or broadband through VIDA, voice monitoring that needs no internet connection.
What a CloudCare program changes for the people it serves, and for the teams that deliver it.
Driven by CloudCare OS and CareSignals.ai. See the systems
Driven by Doppler and IRIS. See the systems
Driven by IRIS, Virtual Clinic agents and Dragon CoPilot. See the systems
Annual run rate at this point in the program.
Run rate, using the cost assumption below.
| Outcome | Start | After a year | Ramp |
|---|---|---|---|
| Patients with a current actionable signal | 35% | 90% | Fast, first four months |
| Avoidable events reached early | 22% | 78% | Begins in month 1 |
| Median lead time before an event | 1 day | 9 days | Same as above |
| Contacts that are proactive | 18% | 65% | Begins in month 2 |
| Result input | Value |
|---|---|
| ED visits per enrolled patient per year | 0.24 |
| Share of those visits that are avoidable | 30% |
| Share of reached avoidable visits actually prevented | 50% |
| Cost of one avoidable ED visit | $3,500 |
Replace each value with your own program’s baseline before using any figure outside a demonstration.
What the platform saves a delivery team and its clients: hours, cost, business won or kept, and time to market. Everything moves with the months slider above. Totals are cumulative since go-live.
engineering hours saved
won or kept since go-live
sooner on every module
Every figure above comes from these inputs. Change them to match your own program.
Hours saved = modules delivered × (traditional hours minus CloudCare hours). Client hours avoided = modules × weeks saved × team size × 40. Business won = pursuits × win-rate lift × contract value. Earlier value = modules × weeks saved × value per week. An engineer-year counts as 1,800 hours. All defaults are illustrative placeholders, not measured results or promises.
Modeled with synthetic data and the assumptions above. Illustrative only: not a forecast, a guarantee, or a clinical or actuarial determination.
The questions every buyer should ask, answered.
| The question | CloudCare’s answer |
|---|---|
| What outcome improves, from what baseline, by when? | V2R bars are set before we start, and measured throughout |
| Technology, process, culture and sequence? | Clinicians set the bars; start small, then scale what works |
| Will people accept it? What work does it remove? | It removes work people feel, starting with the most repetitive steps |
| Lower the cost of reprocessing data, or add a silo? | Data mapped where it lives and processed fast; no new silo |
| Can we change tools later without losing data? | Sovereign: you own the data, the index and the models, with open-source AI |
| Will the partner be measured on outcomes? | Kill criteria and a scored V2R record on every capability |
CloudCare connects to the data healthcare already has, turns a vision into working software fast, runs it in production without a rebuild, acts on it responsibly, and absorbs change instead of breaking.
Integrators, agencies and care teams, from the first prototype to production.
Use Cases · Overview
Use cases are grouped into four themes. Pick your audience to narrow the list, or use the map to see where each theme fits. IRIS can explain any of them.
Themes from federal health leaders, and what each means for CloudCare.
Value has to show in weeks, on the systems agencies already run.
Remove middleware, duplicate copies and manual handoffs.
No rip-and-replace: connect to existing EHR and agency systems.
Tie AI to measurable time saved, not demos.
The agency owns its data, models and deployment.
FHIR, SNOMED CT, LOINC and RxNorm built in, not added on.
The patents behind CloudCare, listed in one place.
Tap a bubble to see what each stage means.
Independent patent appraisal
In December 2024, Ocean Tomo appraised nine issued Life365 U.S. patents at $777 million.
The appraisal covers those patents only. It does not cover patents issued since, allowed or pending applications, or the provisional applications, which have not been valued. It is an appraisal of patents, not of the company.
U.S. patents and pending applications held by Life365, Inc., grouped by technology. Each issued patent links to the USPTO.
| Patent | Title | Group | Issued | At the USPTO |
|---|
| Application | Title | Group | Filed | At the USPTO |
|---|
Copyright © 2026 Life365, Inc. All rights reserved. Patented. Confidential. CloudCare® is a registered trademark of Life365, Inc. This page and the information on it are the property of Life365, Inc.
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Quick answers to what people usually ask first. IRIS, on the right edge of the page, can go deeper on any of them.
A guided tour of V2R: from the artifacts of an existing system to a working system on CloudCare OS.
Demonstration environment. All patient data shown is synthetic.