Go-To-Market Overview¶
Document Purpose: This document outlines the go-to-market strategy for Entheory.AI, covering target markets, customer segments, value propositions, and sales motions based on our platform capabilities.
Executive Summary¶
Entheory.AI targets the Indian healthcare market with a modular, AI-powered longitudinal oncology record system. Our GTM strategy focuses on demonstrating immediate clinical value through pilot deployments, then scaling through hospital networks and ecosystem partnerships.
Related Documents: - Vision & Strategy – Long-term vision and strategic objectives - Product Positioning – Target users and differentiation - Personas – Detailed user personas
1. Target Geographies¶
Primary Market: India¶
- Focus: Tier-1 and Tier-2 cities with established oncology centers
- Initial Cities: Mumbai, Delhi NCR, Bangalore, Chennai, Hyderabad, Kolkata, Pune
- Expansion Path: Gujarat, Tamil Nadu, Kerala cancer centers
Why India First?¶
| Factor | Advantage |
|---|---|
| Market Size | 1.4M+ new cancer cases annually |
| Data Fragmentation | Severe—most hospitals operate 4-6 disconnected systems |
| Language Diversity | Bilingual (English + Hindi) OCR/ASR is a key differentiator |
| Regulatory Alignment | ABDM/ABHA ecosystem creates interoperability opportunities |
| Cost Sensitivity | SaaS model (₹500-2000/patient/year) fits hospital budgets |
2. Target Customer Profiles¶
2.1 Primary Segments¶
| Segment | Characteristics | Use Case Focus |
|---|---|---|
| Cancer Specialty Hospitals | 100-500 beds, dedicated oncology departments | Full platform: Ingestion, Imaging, Oncology workflows, Tumor Board |
| Multi-Specialty Hospital Chains | 500+ beds, oncology as one department | Modular deployment: Start with Imaging + Labs, expand to Oncology |
| Regional Cancer Centers | Government/trust-run, high patient volume | ABDM compliance, Hindi support, cost-effective deployment |
| Oncology Research Institutes | Academic focus, clinical trials | Analytics, NLP for cohort identification, trial eligibility screening |
2.2 Decision Makers¶
| Role | Priority Concerns | How We Address |
|---|---|---|
| Medical Director / Chief Oncologist | Clinical value, time savings, data accuracy | Longitudinal view, 5-10 min saved per patient, >90% OCR accuracy |
| Hospital CIO / IT Head | Integration complexity, security, uptime | HL7/FHIR adapters, <2 week deployment, 99.5% SLA |
| CEO / CFO | ROI, scalability, vendor lock-in | SaaS pricing, works with existing EMRs, no rip-and-replace |
| Quality & Compliance Officer | NABH, ABDM, DPDP compliance | Built-in compliance workflows, audit trails |
3. Value Proposition¶
For Oncologists¶
"See your patient's complete cancer journey—labs, imaging, genomics, therapy—in one unified timeline, saving 15+ minutes per consultation."
For Hospital IT¶
"Integrate with your existing EMR, LIS, and PACS in under 2 weeks. No system replacement, just a smart overlay."
For Hospital Leadership¶
"Demonstrate quality outcomes, improve tumor board efficiency, and meet ABDM compliance without disrupting current workflows."
Platform Capabilities by Use Case Group¶
| Capability | Use Cases | Clinical Impact |
|---|---|---|
| Data Ingestion | 20 UCs (HL7, PACS, CSV) | Unified data from fragmented sources |
| Imaging Integration | 7 UCs (DICOM, viewer, AI scheduling) | Radiology reports linked to patient timeline |
| Oncology Workflows | 49 UCs (diagnosis, treatment, genomics) | TNM staging, tumor markers, RECIST tracking |
| Processing (OCR/ASR) | 12 UCs (Hindi + English) | Structured data from scanned docs and audio |
| Notifications | 7 UCs (multi-channel alerts) | Critical results reach clinicians instantly |
| Pharmacy & Meds | 8 UCs (prescriptions, interactions) | Drug-drug interaction checks at order time |
| Operations & Monitoring | 9 UCs (pipeline health, DR drills) | 99.5% uptime with proactive alerting |
| Security & Compliance | 7 UCs (DPDP, audit trails) | ABDM-ready, consent management built-in |
4. Sales Motion¶
Phase 1: Pilot-Led Sales (Months 1-12)¶
Goal: Prove clinical value at 3-5 reference hospitals
| Stage | Duration | Activities |
|---|---|---|
| Discovery | 2-4 weeks | Stakeholder interviews, system audit, integration assessment |
| POC Proposal | 1-2 weeks | Scope definition, success metrics, pricing |
| Pilot Deployment | 4-8 weeks | Integration, training, go-live support |
| Value Demonstration | 8-12 weeks | Measure time saved, data completeness, clinician satisfaction |
| Contract Conversion | 2-4 weeks | Annual subscription negotiation |
Phase 2: Partner-Enabled Growth (Months 12-24)¶
Goal: Scale through EMR/PACS vendor partnerships
- EMR Partners: Pre-built integrations with top 5 Indian EMR vendors
- PACS Partners: DICOM adapter partnerships for seamless imaging
- Cloud Partners: AWS/Azure/GCP healthcare marketplace listings
- System Integrators: Training and certification for deployment partners
Phase 3: Self-Service Expansion (Months 24+)¶
Goal: Enable smaller clinics with minimal touch
- Self-service onboarding portal
- Template-based configuration for common setups
- Usage-based pricing for low-volume facilities
5. Competitive Positioning¶
Competitive Landscape¶
| Competitor Type | Examples | Our Advantage |
|---|---|---|
| Full EMR Vendors | HIMS, 3M, Epic derivatives | We layer on existing systems—no rip-and-replace |
| Indian EMR Vendors | Practo, eHospital, BahmniCare | Specialized oncology workflows, not generic |
| International Oncology Systems | Flatiron, Tempus | India-first (Hindi support, ABDM, pricing) |
| Point Solutions | PACS viewers, lab systems | Unified longitudinal view across modalities |
Key Differentiators¶
- India-First Design
- Hindi OCR/ASR out of the box
- ABDM/ABHA native integration
-
DPDP Act compliant consent workflows
-
Non-Disruptive Integration
- Works with existing EMR, LIS, PACS
- HL7 v2 + FHIR R4 adapters
-
<2 week deployment timeline
-
Oncology Specialization
- 49 oncology-specific use cases
- TNM staging, tumor markers, RECIST
-
Tumor board packet generation
-
Transparent AI
- Rule-based alerts (no black box)
- Clear provenance for all data
- Clinician-controlled workflows
6. Marketing Strategy¶
Content Marketing¶
- Case Studies: Publish pilot hospital results (time saved, adoption rates)
- Clinical Whitepapers: Longitudinal oncology data impact on outcomes
- Technical Blog: Integration patterns, ABDM guidance, OCR accuracy benchmarks
Events & Conferences¶
- AIOCD (All India Oncology Conference)
- HIMSS India chapters
- FICCI Healthcare events
- Hospital association forums
Digital Presence¶
- SEO for "oncology EMR India", "cancer data platform", "ABDM oncology"
- LinkedIn thought leadership (founders + clinical advisors)
- YouTube demo videos and webinars
Referral Program¶
- Clinician referral incentives
- Hospital network expansion bonuses
- Partner channel commissions
7. Success Metrics¶
Year 1 Targets¶
| Metric | Target |
|---|---|
| Pilot Hospitals | 3-5 |
| Active Patients | 5,000+ |
| Clinician DAU | 50+ daily active users |
| Time Saved/Patient | 5-10 minutes |
| NPS Score | >40 |
| Revenue (ARR) | ₹50L |
Year 2 Targets¶
| Metric | Target |
|---|---|
| Hospitals Deployed | 30+ |
| Active Patients | 50,000+ |
| Clinician DAU | 300+ |
| Market Share (Tier-1 Oncology) | 10-15% |
| Revenue (ARR) | ₹2-5 Cr |
| Partner Integrations | 3-5 EMR/PACS vendors |
Document Owner: GTM & Business Development Team
Last Updated: 2024-12-09
Next Review: Quarterly (post-pilot evaluations)