VehaVirtual ICU · Understanding Deck
VEHA HEALTH · INTERNAL DECK

Veha Virtual ICU
A tele‑ICU, reimagined.

Specialist care that travels through cameras, smart glasses and the cloud. This deck walks through our understanding, the technical stack, the architecture, the development plan, and a clear cost picture.

Prepared by Tiruveedula Jagadeesh
v1.0 · 9 May 2026 · Hyderabad, India

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01 · The story

A small town hospital. 2 a.m. One critical bed.

A 58‑year‑old farmer is wheeled into a 30‑bed hospital in Anantapur. His oxygen is dropping. The duty doctor is a brilliant generalist — but the nearest intensivist is 180 km away. The next four hours will decide everything. This is the moment Veha was built for.

Today's reality

  • Tier‑2/3 hospitals can't staff intensivists 24×7
  • Patients are referred late, transferred, or lost
  • Specialists exist — but not where they're needed

What Veha changes

  • One intensivist, many ICU beds, many hospitals
  • PTZ cameras + AR glasses become their eyes
  • AI scribe + summary become their second pair of hands

The outcome

  • Specialist eyes on the bedside in under 60 seconds
  • Continuous recording — every decision is auditable
  • A discharge summary that writes itself
⏱ 24×7 coverage 🏥 multi‑hospital 🔒 audit‑grade 💸 OPEX, not CAPEX 🤖 AI‑augmented
02 · Personas

Four humans, one mission

Super Admin

Onboards hospitals, assigns doctor pool, owns billing & platform health.

Hospital Admin

Adds wards, beds, devices and staff. Manages shifts and reports.

Virtual Doctor

Watches live, drives PTZ, talks via glasses, signs the discharge summary.

On‑site Staff

Wears glasses, admits patient, executes the doctor's instructions.

Out of MVP: 3rd‑party EMR connectors, mobile apps for staff/doctor (deferred).

03 · End‑to‑end flow

From admit ⟶ live monitor ⟶ discharge summary

1 · Admit
Staff registers patient → recording starts.
2 · Monitor
Doctor sees PTZ + glasses + transcript.
3 · Guide
Push‑to‑talk, alerts, pinned notes.
4 · Discharge
AI drafts summary, doctor signs, blob sealed.
04 · Tech stack

The lean stack

🧠 Backend

Node.js + NestJS (TypeScript) — same language across BE/FE. Spring Boot acceptable if team is Java‑heavy.

REST + WebSocket (PTZ, alerts, presence).

🐘 Database

PostgreSQL on Azure Flexible Server (Burstable B1ms). JSONB for flexible clinical fields.

Redis Basic C0 for sessions & presence.

🎨 UI

React 18 + Vite + TypeScript + TailwindCSS + shadcn/ui. Doctor console + Admin portals.

No mobile app in MVP.

📹 Realtime media

LiveKit (self‑hosted, 1 small VM) for WebRTC. go2rtc / MediaMTX on edge gateway converts RTSP→WebRTC.

☁️ Azure platform

Container Apps (scale‑to‑1), Blob Storage (Hot→Cool→Archive), Front Door + WAF, Key Vault.

🤖 AI

Azure Speech (VAD‑gated STT) + Azure OpenAI gpt‑4o‑mini for discharge summaries & "ask the chart".

Build time: Copilot Business + Cursor + ChatGPT Team — projected ~25% engineering compression.

05 · Devices

Eyes on the ground

PTZ IP camera

  • 1080p, IR, PoE, ONVIF compliant
  • Pan / Tilt / Zoom + 8 named presets
  • Doctor‑controlled via secure WebSocket relay
  • 1 per ICU bed

AR smart glasses

  • RealWear / Vuzix‑class (Android)
  • 1‑way video + 2‑way audio over WebRTC
  • Push‑to‑talk; SOS long‑press
  • 2 pairs per hospital (rotation + spare battery)

🛰️ Edge gateway (1 per hospital)

Mini PC on hospital LAN. Runs go2rtc/MediaMTX + buffer. Cameras never face the public internet; the gateway pushes outbound TLS to LiveKit.

06 · Architecture

How the pieces talk

🏥 Hospital LAN PTZ Camera ×5 (1/bed) AR Glasses ×2 Edge Gateway go2rtc / MediaMTX RTSP → WebRTC Nurse Station Browser (admit / vitals) ☁️ Azure (South India) Front Door + WAF TLS, routing Container Apps NestJS API + workers LiveKit VM WebRTC SFU PostgreSQL Flexible Server Redis Cache Sessions, presence Blob Storage Hot/Cool/Archive Azure Speech (STT) VAD‑gated, multilingual Azure OpenAI · gpt‑4o‑mini Discharge summary, ask‑the‑chart App Insights + Monitor Logs, metrics, alerts Key Vault + Defender Secrets, posture 🖥️ Clients Doctor Web React + PTZ console Admin Web Tenant + billing Nurse Browser Admit + vitals 📱 Mobile (later) Out of MVP
07 · Data flow

Where every byte goes

📹 Live camera

  1. Camera RTSP → Edge GW
  2. Edge GW transcodes → WebRTC publish to LiveKit
  3. Doctor browser subscribes (≤ 1.5 s latency)
  4. LiveKit Egress writes segmented MP4 to Blob

🥽 Glasses

  1. WebRTC peer direct to LiveKit (Wi‑Fi/4G)
  2. Doctor subscribes; 2‑way audio always on
  3. Audio also tee'd to Azure Speech (VAD‑gated)
  4. Transcript appended to patient timeline (Postgres)

🎮 PTZ control

  1. Doctor click → WebSocket to API
  2. API authorises (control token, RBAC)
  3. Edge GW receives ONVIF command, drives camera
  4. Action audited in append‑only log

📝 Discharge summary

  1. Doctor clicks "draft summary"
  2. Worker pulls transcript + vitals + notes
  3. PHI redaction → Azure OpenAI → structured JSON
  4. Doctor edits + e‑signs → PDF + sealed in Blob
08 · Features

What ships in the MVP

🔐 Identity & RBAC

JWT + email OTP, MFA for doctor/admin, per‑bed ACL, full PHI audit log.

🏥 Tenancy

Hospital → ward → bed → device hierarchy. Schema‑per‑tenant‑ready.

👁️ Live monitor

PTZ + glasses + vitals + transcript on one screen. Multi‑doctor view, single PTZ control token.

🎙️ Real‑time STT

English + 1 Indian language, speaker diarization, searchable transcripts.

🪄 AI summary

gpt‑4o‑mini drafts SOAP discharge summary, doctor edits & signs.

🚨 Alerts

SOS, "code blue" keyword, device offline, doctor‑raised. Email + SMS + browser push.

📼 Recording

Continuous while admitted. Hot 7 d → Cool 30 d → Archive.

🧾 Patient lifecycle

Admit → vitals → notes → orders → discharge. PDF + FHIR export.

📊 Reports

Occupancy, response time, alert volumes, per‑tenant cost.

09 · Non‑functional

Numbers we hold ourselves to

99.9%

Control plane uptime

99.5% on media plane in MVP.

≤ 1.5 s

Camera glass‑to‑glass

≤ 800 ms audio RTT on glasses.

≤ 300 ms

API p95

Under typical pilot load.

AES‑256

At rest

TLS 1.2+ in transit; signed URLs for media.

7 yrs

Audit retention

Append‑only; tamper‑evident hashes on recordings.

India

Data residency

DPDP‑aligned, HIPAA‑ready, ISO 27001 path.

10 · Development plan

Five phases, one pilot

0 Discovery 4 wks 1a Core platform 5 wks 1b Streaming + recording 6 wks 1c Clinical + AI 6 wks 2 Hardening + go‑live 5 wks
~6 months

≈ 26 weeks from kickoff to first hospital go‑live, with an AI‑augmented team of ~5.5 FTE.

11 · Team

Who builds it

Tech Lead

×1 · Owns BE + FE + Azure.

Full‑stack

×2 · Node + React, AI‑augmented.

Streaming / Device

×1 · LiveKit, ONVIF, glasses.

QA + DevOps

×1 · Playwright + GitHub Actions.

UI/UX

×0.5 · Figma + shadcn.

PM + Clinical SME

×0.5 · Workflow validation.

Copilot + Cursor

≈ 25% velocity boost.

Effective FTE

~5.5 billable.

12 · Costs

Two buckets — simple billing for the hospital

A · ONE‑TIME (PLATFORM)

Build cost

Server & tools to build the platform once. Reused for every future hospital. Development effort billed separately.

~₹1.45 L

6 months · server + tools only

B · PER HOSPITAL

Hospital cost

Everything the hospital pays for, in two parts:

  • One‑time: ~₹3 L onboarding hardware — cameras, worn devices, install, GST.
  • Monthly: ~₹33 k running cloud — 5 beds · ~₹6,650 / bed / month.
  • Per active bed‑hour: ~₹40 at full encounter mode.

Doctor fees, support staff and clinical costs sit outside these buckets and feed the subscription price directly.

12A · Server cost during build

Bucket A — Server & tools cost during build

Lean setup. Postgres runs on a single self‑managed VM, no managed databases, no Media Services, no AKS during build. Founder / engineering salaries are absorbed by the team and excluded. We upgrade infra only when real app usage demands it.

Line itemDetail₹ (6 mo)
Single VM (dev + staging)1× Hetzner / Contabo CX22, Docker, self‑managed Postgres, Nginx, MinIO · 6 mo × ₹1,5009,000
Object / blob storageMinIO on same VM · ₹500/mo backup snapshot to B2 / Wasabi3,000
Domain + SSL.in domain · Let's Encrypt SSL (auto‑renew)2,000
Email / transactionalResend / Brevo starter · paid SMS OTP top‑up3,000
ObservabilitySelf‑hosted Grafana + Loki on same VM · Sentry starter0
AI coding toolingCopilot Pro+ × 3 seats · 6 mo @ $39 ≈ ₹3,315/seat/mo59,670
Design + repoFigma starter, GitHub · 1 paid seat5,000
Pen‑test (lite, before pilot)One‑off external review before go‑live50,000
Contingency (10%)13,170
Total Bucket A (server + tooling, 6 mo)~₹1,45,000

Upgrade path (only when real usage shows up): single VM ➜ managed Postgres on Azure / DO ➜ AKS + Media Services ➜ multi‑region. We pay for managed cloud only after the first paying hospital is live.

12B · Running cost per bed‑hour

Bucket B — Running cost, per bed per hour

What makes up one bed‑hour

What we pay for₹ / hr
Saving the camera recording0.5
Streaming live video to the doctor5.0
Video room (doctor ↔ bedside nurse ↔ ICU team)5.5
Voice‑to‑text for nurse rounds (~10 min)18.0
Auto handoff summary (every 3 days)1.5
Shared platform cost4.5
Safety buffer (15%)5.0
Total per active bed‑hour~₹40

What the bed is doing right now

What’s happening at the bed₹ / hr
Bed quiet — only camera recording6
Doctor watching the live video15
Nurse on glasses, dictating notes27
Full active encounter40
How we use this: hospital subscription tiers are priced safely above this number, depending on how many hours per day the doctor is on call.
12B · Running cost monthly

Monthly running bill (1 hospital, 5 beds)

What we pay forWhat it does for the hospital₹ / mo
App serversRun the doctor / nurse screens, login, alerts2,900
Live video roomCarries doctor ↔ patient ↔ nurse video calls2,490
Patient + vitals databaseStores every reading, note and alert, with daily backup2,075
Fast memory cacheKeeps screens snappy under load1,410
Recent recordings (last 7 days)Fast playback for any bed in the last week250
Older recordings (up to 1 month)Slower but cheaper storage for last month500
Long‑term archiveCompliance archive of everything older915
Internet bandwidthSending video out to doctors’ phones / laptops1,825
Voice‑to‑text for nurse rounds~14,400 minutes of dictation per month, auto‑typed11,950
AI handoff summary72‑hour shift summary written for the next doctor85
Email + OTP SMSLogin OTPs, escalation alerts to on‑call doctor / ICU lead580
Security, login, audit, monitoringFirewall, single sign‑on, audit log, error alerts2,905
Backup retentionLong‑term legal retention of backups830
Safety buffer (~15%)Reserve for spikes4,320
Total~₹6,650 / bed / mo~33,000
12B · Shared vs dedicated

Two ways to run it: shared or dedicated

Same software, same features. The only thing that changes is whether your hospital sits inside a shared building or in its own private one.

MODEL A · SHARED

Shared platform (recommended for pilot)

All hospitals run on the same Veha cloud. Each hospital's data is locked behind its own login and access rules — no hospital can see another's data.

  • Privacy: strong, software‑level. Logical separation per hospital.
  • Billing: one platform bill split across hospitals — cheaper for everyone.
  • Cost: ~₹33 k / month for 5 beds (~₹6,650 per bed).
  • Setup time: live in 1 week.
  • Best for: pilots, small to mid‑size hospitals, anyone who wants the lowest possible price.
MODEL B · DEDICATED

Private setup, just for your hospital

Your hospital gets its own private cloud space — its own database, its own video room, its own storage. Nothing is shared with other hospitals.

  • Privacy: very clear — physically separate stack you can audit.
  • Billing: a single line‑item bill that belongs only to you.
  • Cost: ~₹65 k – ₹85 k / month for 5 beds (~2× the shared model).
  • Setup time: 2–3 weeks.
  • Best for: large hospitals, group practices, anyone with strict internal data rules.
Our recommendation: start on the shared model for the pilot to keep costs and risk low. Once usage is proven, any hospital can request a one‑click move to its own dedicated setup — same data, same screens, just a private address.
13 · Scale

Cost per bed drops as we grow

₹ / mo beds 8,000 6,000 4,000 2,000 ₹6,650 ₹4,820 ₹3,820 ₹3,320 ₹2,990 5 10 50 100 250 Per‑bed cloud cost (₹ / month) vs total beds on platform
As more beds use the platform, fixed costs (servers, video room, monitoring) get shared across more beds — so per‑bed cost falls. Variable costs (voice‑to‑text, storage) grow only with actual usage.
14 · Security

Built for healthcare, audit‑ready

Encryption

TLS 1.2+ in transit, AES‑256 at rest, signed URLs for media.

Identity

JWT + email OTP, MFA mandatory for SuperAdmin & Doctor, per‑bed RBAC.

Data residency

Azure South India only. RA‑GRS for archive.

Audit log

Append‑only PHI access log retained 7 years; tamper‑evident hashes on recordings.

Compliance

DPDP‑aligned, HIPAA‑ready controls, ISO 27001 path post‑GA.

AI guardrails

PHI redaction in prompts, doctor‑in‑the‑loop on every signed artefact.

15 · Risks

What worries us — and how we cope

RiskMitigation
Hospital Wi‑Fi unreliableEdge GW buffers locally; degrade to 480p / audio‑only.
Glasses battery & heat in long shifts2 spare batteries per glass, 4‑hour rotation policy.
ONVIF PTZ quirks across vendorsLock to one certified model in MVP; abstract driver layer.
Cloud cost surprises (egress, STT)Per‑tenant cost dashboard + monthly soft caps + alerts.
Compliance gaps (DPDP/HIPAA)Phase‑0 gap analysis; pilot only under signed BAA + DPA.
AI summary hallucinationDoctor edits & e‑signs; transcript + vitals shown side‑by‑side; PHI redaction.
16 · Cost levers

How we keep cost down by design

📐 Right‑sized video

720p / 1 Mbps for ICU view — doctor‑clear, ~55% lighter on storage than full HD.

🎙️ Smart voice‑to‑text

Transcription runs only when the nurse actually speaks — ~50% saving.

🛰️ Our own video room

We host the video bridge ourselves — flat monthly cost instead of pay‑per‑minute.

🗃️ Tiered recording storage

Last 7 days fast, last 30 days slower & cheaper, older as cold archive — 90%+ saving on long‑term.

🧠 Lean AI model

We use a smaller AI for shift summaries — about 30× cheaper than the top‑tier model, same quality for our use.

📦 Auto‑scale down

Off‑hours, the app servers shrink to a single small instance — we don’t pay for idle capacity.

17 · Headline numbers

The pitch in five numbers

₹1.45 L

Pilot build (server + tools)

6 months · development cost billed separately

₹3 L

Hospital onboarding

5 beds · 2 worn devices · install · incl. GST

₹33 k

Running cost / month

~₹6,650 / bed / month

₹40

per active bed‑hour

full encounter mode

26 wks

To pilot go‑live

~6 months · Phase 0 → hardening

Goal

Save lives where intensivists can't physically be.

18 · Open questions

What we need from you

  1. Which languages should the system understand at launch — English only, or also Telugu / Hindi?
  2. How long must we keep ICU camera recordings, by your hospital policy?
  3. Should we use one approved camera + smart‑glasses model, or are you open to options?
  4. Who buys the in‑ICU hardware — Veha, or the hospital?
  1. How would you like to be billed — per bed, per hour the doctor is on, or per patient?
  2. When can the pilot start, and when do you want it live for patients?
  3. Will Veha provide the on‑call intensivists, or will your hospital?
  4. Any other ICU‑specific rules or vendors we must respect?

Once we have your answers, we lock the scope, the price and the go‑live date in writing — one page, no jargon.

END · Q&A

Thank you

Prepared by Tiruveedula Jagadeesh

v1.0 · 9 May 2026 · Hyderabad, India