SaaS Plans & ROI

Transparent Pricing Grounded in Indian Healthcare Realities

Flexible per-patient or departmental plans. Designed around oncology department heads and multi-hospital tumor boards.

Starter

For Individual Oncologists & Small Clinics

Free / 15 cases trial
  • ✓ 15 patient summaries per month
  • ✓ ABDM M1 Consent Verification
  • ✓ Unstructured PDF OCR Extraction
  • ✓ Standard Web Summary View
Primary Locked Wedge

Team (Tumor Board)

For Multi-Hospital Cancer Centers & Departments

₹4,999 / patient case bundle
  • ✓ Unlimited ABDM M1/M2/M3 Auto-Fetch
  • ✓ Automated Staging Conflict Detection
  • ✓ CarePACS DICOM-Web & Radiology Sync
  • ✓ Coded LOINC / SNOMED Export (FHIR R4)
  • ✓ Tumor Board PDF & CSV Package Export

Enterprise

For Hospital Networks, Chains & CROs

Custom / annual contract
  • ✓ On-Premises or Private India VPC Deployment
  • ✓ Custom BAA & SLA Guarantees (99.9% Uptime)
  • ✓ Custom EMR Adapter Development
  • ✓ Dedicated Clinical Support & Onboarding Engineer
Interactive ROI Engine

Calculate Your Tumor Board Prep Time Saved

See how many administrative hours your oncology department will reclaim each month.

Cases Per Tumor Board Session: 20
Tumor Board Meetings Per Month: 4
*Calculated assuming manual record gathering of 15 mins/case across 3+ hospital systems vs. EntheoryAI automated fetch & conflict detection under 60s per case.
19 hrs/mo
Clinical Prep Time Saved Each Month
₹46,667
Estimated Monthly Physician Productivity Saved
Buyer FAQ

Frequently Asked Security & Integration Questions

Is EntheoryAI ABDM compliant in India?

Yes. EntheoryAI is designed natively around the NHA Ayushman Bharat Digital Mission (ABDM) guidelines for Health Information Providers (HIP) and Health Information Users (HIU). We strictly enforce OTP-based patient consent before fetching records.

Where is patient health data stored?

100% of patient data processing and storage resides on MeitY-empanelled cloud infrastructure located in India (AWS Mumbai region). No clinical data leaves Indian territory.

How does conflict detection work between EMR and PACS?

Our NLP engine parses staging notes in EMR discharge summaries and compares them against DICOM radiology reports and pathology findings. If EMR notes cite Stage III-A but radiologic DICOM notes identify distant metastasis (Stage IV), EntheoryAI flags this discrepancy prominently for the tumor board.

What is the typical deployment timeline for a hospital network?

Because EntheoryAI leverages ABDM and FHIR APIs, initial sandbox deployment for tumor boards takes less than 48 hours. Enterprise on-prem integration typically completes within 2–3 weeks.