Product Positioning
Document Purpose: This document defines how Entheory.AI is positioned in the market—who we serve, what we offer, and why we're different.
1. Product Positioning Statement
For oncologists and cancer care teams at Indian hospitals
Who struggle with fragmented patient data across multiple systems,
Entheory.AI is a longitudinal oncology record platform
That unifies labs, imaging, pathology, genomics, and therapy data into a single timeline.
Unlike traditional EMRs or point solutions,
We integrate non-disruptively with existing systems, support Hindi and English, and are built for ABDM compliance from day one.
2. Target Users
See Also: Detailed User Personas for in-depth profiles, pain points, and workflows.
Primary Users (Daily Active)
| Persona |
Role |
Primary Use Cases |
| Oncologist |
Medical decision maker |
View patient timeline, review trends, approve notes |
| Tumor Board Members |
Multidisciplinary specialists |
Access tumor board packets, review recommendations |
| Clinical Coordinator |
Patient care manager |
Track treatment schedules, manage follow-ups |
| Oncology Nurse |
Care delivery |
View orders, document observations |
Secondary Users (Weekly/Occasional)
| Persona |
Role |
Primary Use Cases |
| Hospital IT Admin |
System administrator |
Monitor integrations, manage users |
| Quality Officer |
Compliance oversight |
Review audits, access reports |
| Research Analyst |
Data analysis |
Query anonymized data, cohort identification |
| Hospital Leadership |
Strategic oversight |
Review adoption metrics, ROI dashboards |
User Needs Matrix
| User |
Pain Point |
How We Solve |
| Oncologist |
15-20 min wasted gathering data per patient |
Single unified timeline, <3 clicks to any data |
| Tumor Board |
Manual packet preparation takes hours |
Auto-generated tumor board packets |
| Coordinator |
Missed follow-ups and care gaps |
Proactive alerts and care gap tracking |
| IT Admin |
Integration projects take months |
<2 week deployment, pre-built adapters |
| Quality Officer |
Manual audit preparation |
Built-in audit trails, NABH-aligned reports |
3. Target Customers
Ideal Customer Profile (ICP)
| Attribute |
Ideal Profile |
| Type |
Cancer specialty hospital or multi-specialty with oncology department |
| Size |
100-500 beds with dedicated oncology |
| Location |
Tier-1 or Tier-2 Indian cities |
| Systems |
Existing EMR + LIS + PACS (fragmented) |
| Tech Maturity |
IT team capable of HL7/FHIR integration |
| Compliance |
NABH accredited or pursuing accreditation |
| Leadership |
Executive sponsor for digital transformation |
Customer Segments
| Segment |
Characteristics |
Entry Point |
| Cancer Specialty Hospitals |
Oncology-focused, high cancer volume |
Full platform deployment |
| Multi-Specialty Chains |
Oncology as one department |
Start with Imaging + Labs, expand |
| Regional Cancer Centers |
Government/trust-run, cost-sensitive |
ABDM compliance + Hindi support |
| Oncology Research Institutes |
Academic focus, clinical trials |
Analytics + cohort identification |
Decision Maker Map
| Role |
Buying Motivation |
Concerns |
| Medical Director |
Clinical efficiency, quality outcomes |
Accuracy, adoption, workflow disruption |
| CIO/IT Head |
Integration simplicity, security |
Technical complexity, vendor lock-in |
| CEO/CFO |
ROI, competitive advantage |
Cost, time to value, scalability |
| Quality Head |
Compliance, audit readiness |
NABH, ABDM, DPDP alignment |
4. Differentiation
vs. Traditional EMRs (HIMS, Epic derivatives)
| Factor |
Traditional EMR |
Entheory.AI |
| Integration |
Replace everything |
Layer on top |
| Deployment |
6-12 months |
2-4 weeks |
| Scope |
General hospital ops |
Specialized oncology |
| Language |
English only |
English + Hindi |
| Data View |
Encounter-based |
Longitudinal timeline |
| Cost |
₹50L-2Cr capex |
SaaS, ₹500-2000 PPPY |
vs. Indian EMR Vendors (Practo, eHospital, BahmniCare)
| Factor |
Indian EMR |
Entheory.AI |
| Specialty Focus |
General purpose |
Oncology-specialized |
| Data Unification |
Within their system |
Across all systems |
| AI/ML |
Basic or none |
OCR, ASR, NLP built-in |
| Longitudinal View |
Limited |
Core feature |
| ABDM Compliance |
Variable |
Native support |
| Factor |
International |
Entheory.AI |
| Pricing |
US/EU pricing |
India-appropriate |
| Language |
English only |
English + Hindi |
| Regulatory |
FDA/CE focused |
ABDM/DPDP focused |
| Local Support |
Remote |
Local presence |
| Integration |
US standards |
Indian HL7/FHIR variants |
vs. Point Solutions (PACS viewers, LIS systems)
| Factor |
Point Solutions |
Entheory.AI |
| Scope |
Single modality |
Unified multi-modality |
| Patient Identity |
Siloed |
Normalized (ABHA ID) |
| Longitudinal |
No |
Yes |
| Alerts |
Within system |
Cross-system clinical alerts |
5. Key Differentiators Summary
1. India-First Design
- Hindi OCR/ASR out of the box
- ABDM/ABHA native integration
- DPDP Act compliant consent workflows
- India-appropriate pricing (SaaS, per-patient)
2. Non-Disruptive Integration
- Works with existing EMR, LIS, PACS
- HL7 v2 + FHIR R4 adapters included
- <2 week deployment timeline
- No rip-and-replace required
3. Oncology Specialization
- 49 oncology-specific use cases
- TNM staging extraction
- Tumor markers trending
- RECIST lesion tracking
- Tumor board packet automation
4. Transparent AI
- Rule-based alerts (no black box for clinical decisions)
- Clear provenance for all data points
- Clinician-controlled workflows
- Manual review queues for low-confidence AI outputs
5. Longitudinal Patient View
- Unified timeline across all modalities
- Lab trends visualization
- Treatment history tracking
- Changes since last visit highlighting
6. Messaging by Audience
For Oncologists
"Stop hunting for data across 4-6 systems. See your patient's complete cancer journey—labs, imaging, genomics, therapy—in one unified timeline."
For Hospital IT
"Integrate with your existing EMR, LIS, and PACS in under 2 weeks. Pre-built HL7 and FHIR adapters. No system replacement needed."
For Hospital Leadership
"Improve oncology care quality, boost tumor board efficiency, and meet ABDM compliance—without disrupting your current workflows."
For Patients/Families (Future)
"All your cancer care records in one place, accessible via ABHA. Share your history with any doctor, anywhere in India."
7. Use Case Validation
Our positioning is backed by 200+ developer use cases across:
| Category |
Use Cases |
Key Capabilities |
| Ingestion |
20 |
HL7, FHIR, PACS, CSV integration |
| Imaging |
7 |
DICOM processing, viewer, AI scheduling |
| Oncology |
49 |
Diagnosis, treatment, genomics, care coordination |
| Processing |
12 |
OCR (Hindi + English), ASR, NLP |
| Notifications |
7 |
Multi-channel alerts, patient engagement |
| Security |
7 |
DPDP compliance, audit trails |
| Operations |
9 |
Pipeline monitoring, DR, job orchestration |
| Hospital Workflows |
50+ |
Pharmacy, Bed Mgmt, OT, Blood Bank, Telemedicine |
Document Owner: Product & Strategy Team
Last Updated: 2024-12-09
Next Review: Quarterly (align with GTM updates)