Patient registration & identity
Register patients with unique medical record numbers and build one longitudinal record: demographics, visits, allergies, and conditions.
Built on OpenMRS · Aagamivaidya AI
A complete hospital system for your network, with AI your doctors can actually trust.
Aagami CareOS runs each hospital on its own full clinical system built on OpenMRS, from registration and charting to orders and dispensing. On top of it sit two AI capabilities, documentation and pharmacy assistance, and nothing either one produces reaches the record until a doctor reviews and signs it.
- Reaches the record
- 100%only after a doctor signs
- Patient data in the AI audit
- 0zero PHI ever logged
- Production monitoring
- 24/7uptime you can check
gateway · audit record
launch.granted hospital=hospital-gamma doctor=dr-rao
consent.recorded recording=yes ai_processing=yes
draft.generated sections=6 model=note-writer
note.approved approved_by=dr-rao
writeback.commit type=visit_note orders=2
writeback.done idempotent=true phi=redacted
VerifiedSigned by the doctor · 0 PHI in audit
6 events logged
1 approval gate
0 PHI in audit
The complete system
It's your whole hospital, not just the notes.
Aagami CareOS runs each hospital on its own full clinical system, built on OpenMRS. Your staff register patients, chart care, place orders, and dispense medication in one modern workspace. The AI rides on top of that system; it never replaces it.
Clinical charting
Doctors and nurses record vitals, observations, diagnoses, and care plans in a modern web workspace, visit by visit.
Orders & medications
Place drug and care orders against each hospital's own formulary, with coded medications drawn from a managed drug list.
Pharmacy & dispensing
Orders flow into a dispensing workspace where pharmacy reviews and fills them, backed by formulary-driven safety checks.
Branded clinical workspace
Each hospital gets its own colours, logo, and OpenMRS 3 workspace through configuration, never a separate code fork.
Your whole network, one platform
Stand up and run every hospital from shared templates, each on its own isolated stack, instead of a patchwork of one-off systems.
Flagship · AI clinical documentation
Your doctors talk to patients. The AI does the typing.
Documentation is the work doctors resent most. Aagamivaidya lifts it off them. It records the visit, drafts the note, and hands it back for sign-off, all in one flow that begins and ends inside the patient's chart. Nothing is recorded until a doctor approves it.
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1
Launch from the encounter
Open the patient's chart and start Aagamivaidya in a click. The hospital, doctor, patient, and visit travel along automatically, so the doctor never re-enters a thing.
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2
Confirm consent
The patient agrees to be recorded and to AI processing before a second of audio is captured. Withdraw that consent and recording and drafting stop immediately.
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3
Record & transcribe
The doctor simply has the conversation while Aagamivaidya transcribes in the background. The raw audio and transcript stay on the AI side of the boundary, never in the record.
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4
The AI drafts the note
In seconds, out comes a structured draft: complaints, history, exam, assessment, plan, follow-up, and medication orders. At this point it is only a proposal.
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5
The doctor reviews & signs
The doctor reads the draft, fixes anything that's off, and signs it. That signature is the one and only way anything enters the record, and the doctor stays the final word.
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6
Written back, safely
Once approved, the note lands in the chart and its medications appear as orders in the Dispensing tab, written once and never duplicated, even if a step is retried.
The boundary
Nothing the AI does happens in the dark.
Aagamivaidya never runs inside your hospital's systems. Before any launch or write-back goes anywhere, it passes through a checkpoint you control, where it is authorized, consent-checked, rate-limited, and logged. The audio, the transcript, and the unapproved draft never enter the record, so the chart holds only what a clinician verified.
Audited gateway
Every AI action, from launches and approvals to write-backs, safety checks, overrides, denials, and failures, is recorded to a tamper-evident trail that carries no patient data. If you ever have to answer "what did the AI do, and who approved it?", the answer is already written down.
Patient encounter
clinical record
Platform gateway
consent · policy · audit
Aagamivaidya AI
transcribe · draft
Doctor approves
review · edit · sign
Written to record
Visit Note · drug orders
Drug conflict
Formulary OK
AI context
"Aagamivaidya notes: Amoxicillin 500 mg. Please confirm against this patient's recorded penicillin sensitivity."
Flagship · AI pharmacy assistance
Your formulary holds the hard stops. The AI only ever explains.
The second place AI earns its keep is prescribing, and the same boundary applies. Every check is scoped to one hospital, permission-checked, rate-limited, and logged, and the hard stops come from your formulary and drug rules, not from a model. The AI adds context when an indication is unclear, but on its own it can only raise a flag. It can never block or override an order by itself.
- Hard stopDuplicate & therapeutic duplication
- Hard stopAllergy & contraindication
- FlagDose outlier vs formulary
- FlagInsufficient formulary mapping
- AI contextUnresolved indication mismatch
- RequiredPharmacist confirmation & override audit
Privacy & isolation
One hospital's data never touches another's.
Every hospital in your network runs on its own stack, with its own application, database, storage, and secrets. Patient data is never pooled or shared between sites, and we prove that isolation with two synthetic hospitals before any go-live.
hospital-alpha
App
Database
Storage
hospital-beta
App
Database
Storage
hospital-gamma
App
Database
Storage
Unique IDs, ports, domains, credentials, and tokens. No shared clinical database.
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Isolated databases
Your patients, your database. Each hospital's records sit behind their own boundary, never co-mingled with another site's, so there is no cross-tenant data to leak in the first place.
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Config-driven onboarding
Adding a hospital is a validated configuration built from shared templates, never a forked codebase. Every site is stood up the same proven way, so a new hospital is not a new risk.
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Per-hospital observability
Each site gets its own logs, health checks, dashboards, and metrics, all sanitized so patient data never surfaces in an operational view.
Proof, not promises
Don't trust the claims. Check them.
Readiness on this platform is not a marketing word. Every claim, whether a backup, a release sign-off, or an uptime figure, points to something your team can open: a command, a log, or a saved artifact.
Uptime transparency
A public, live status board for every production hospital node, showing real telemetry your clinical IT team can verify without asking us.
See live uptimeGated releases & rollback
Pinned images, rendered configs, staging evidence, and a human production sign-off are all required before a release ships, and each one carries its own rollback plan.
backup verified · approval gatedBranded O3 interface
Each hospital gets its own colours, logo, and clinical workspace through configuration and extension slots, never a fork to maintain.
Live status
Always on, and you don't have to take our word for it.
Trust should be checkable, not just claimed. We monitor the production hospital stacks around the clock, and the view below is live, pulled straight from the same public status page our operators watch.
Loading live uptime from UptimeRobot… If it does not appear, view it directly at stats.uptimerobot.com.
Ready to bring AI into your hospital, safely?
In a walkthrough we stand up a hospital in front of you, follow a note from the consultation through the gateway and the doctor's approval into the record, and show you the readiness evidence your team will want before committing to anything.