Seven Demos That Prove THB Is AI-Native, Not AI-Washed
50,000 synthetic patients. 18 months of realistic clinical data. Every API call hits the real platform. Nothing mocked. Nothing pre-recorded. If it breaks, you will see it break.
The Demo Narrative
One continuous story across seven demos. Raw hospital data goes in. Measurable patient outcomes come out. You watch the whole journey.
Demo Architecture: What Runs Underneath
Same architecture that runs in production. Nothing mocked. Nothing pre-recorded. Same code path, every time.
Patient 360 Intelligence View
The Story
A patient calls the hospital front desk. The operator types a phone number. Instantly, everything appears: demographics, visit history across departments, lab trends with flags, medications with interaction alerts, risk scores, care gaps, journey timeline, communication preferences. Not a dashboard. A living patient record assembled from three fragmented hospital systems -- right there, on one screen, faster than the operator can finish saying hello.
What You See
- 1Phone number search -- full patient profile loads instantly
- 2Demographics unified from three separate hospital systems
- 3Visit timeline spanning departments with clinical context
- 4Lab results with trend lines and clinical flags that actually mean something
- 5Medications with drug interaction warnings surfaced automatically
- 6Risk score with the 15 factors that drove it
- 7Open care gaps with what to do about each one
- 8Communication preferences and consent status -- before you pick up the phone
What Happens Underneath
- •NovaHub serves pre-assembled patient views in real time -- the data is already waiting
- •MDM resolved this patient across 3 systems into 1 golden record before the call came in
- •Lab values classified against clinical ranges and flagged automatically at ingestion time
- •Risk scores computed from 15+ clinical factors -- not at query time, in the pipeline
- •200+ protocol definitions ran against patient history overnight to find every open care gap
AI Clinical Follow-up Engine
The Story
8 AM. The follow-up coordinator opens their morning brief. The system already ran overnight: 47 patients discharged yesterday need 48-hour check-ins, 23 diabetics are overdue for HbA1c, 12 post-surgical patients need wound care follow-ups. For each one, the AI drafted a message in the patient's language -- Hindi or English -- picked the right channel, and calculated the revenue impact. The coordinator tweaks one message, hits send. Hundreds of clinically grounded, personalized messages go out in minutes. That is the 5.3x ROI -- not from guessing, from knowing.
What You See
- 1Morning brief with follow-up cohorts ranked by revenue impact
- 2AI-drafted messages in Hindi and English -- grounded in actual clinical data
- 3Channel picked per patient: WhatsApp, SMS, or call based on their preference
- 4Revenue impact per cohort and per message -- not a guess, a calculation
- 5One click to approve and send
- 6Delivery tracking and response monitoring in real time
What Happens Underneath
- •Cohort engine ran overnight -- segmented patients by discharge date, pending tests, open care gaps
- •Foundation LLM drafts messages using actual patient clinical context plus hospital templates
- •Language picked from patient profile metadata. Hindi or English. Not guessed.
- •Revenue model: visit probability x average visit value x historical conversion = 5.3x ROI
- •Dispatch through WhatsApp Business API, SMS gateway, and outbound call system simultaneously
Natural Language Analytics
The Story
The hospital CEO walks into a board meeting. No BI team was consulted. They type: 'Show me diabetic patients overdue for HbA1c in the last 6 months.' Seconds later: 847 patients, filtered by department, trend charts, recommended actions. Follow-up question: 'What is the revenue impact of closing these gaps?' Answer: Rs.34.2 lakhs in potential follow-up revenue. No SQL. No BI tool. No waiting for someone to build a report.
What You See
- 1Type a question in English or Hindi. That is it.
- 2Structured response: tables, charts, summary -- not a chatbot paragraph
- 3Follow-up questions that remember what you just asked
- 4Query explanation showing exactly how the system interpreted the question
- 5Export to share with the board
What Happens Underneath
- •The LLM never touches the database. It reasons over pre-computed, verified metrics from DataCloud.
- •Semantic layer translates natural language to clinical data dimensions -- no prompt hacking
- •Every number is traceable to a specific patient record. Zero hallucination risk on metrics.
- •Complex questions decomposed into sub-queries, each verified independently
- •Hindi NLP pipeline with medical terminology mapping built in
Care Gap Detection & Closure
The Story
The quality team opens their dashboard. 1,247 active gaps. 312 diabetics overdue for HbA1c. 89 hypertensive patients missing BP checks. 156 with incomplete vaccinations. 78 women overdue for mammography. Each gap has a priority score, a recommended action, and a revenue number attached. The team picks the highest-priority gaps and initiates automated outreach. Clinical quality improvement that also generates Rs.1.41 Cr in revenue opportunity. That is the point -- quality and revenue are not separate goals.
What You See
- 11,247 active gaps across diabetes, hypertension, cardiac, oncology, and preventive care
- 2Priority = clinical urgency x revenue impact x closure probability. Not a vague ranking.
- 3Drill into any patient: what gap, what to do, what it is worth
- 4Hit a button, outreach starts automatically for gap closure
- 5Rs.1.41 Cr in quantified revenue opportunity sitting in those gaps
- 6Gap closure trends over time -- are we actually getting better?
What Happens Underneath
- •200+ care gap definitions encoded as clinical protocol configs -- not SQL queries someone wrote once
- •Detection runs against complete patient history: visits, labs, meds, procedures
- •Priority scoring: clinical urgency weighted against operational feasibility and closure probability
- •Revenue model: gap type x procedure value x conversion rate. Math, not guesswork.
- •Gaps feed directly into PEP's follow-up engine for automated outreach
Hospital Onboarding in 48 Hours
The Story
A new hospital signs up. Within hours -- not months -- their data flows through THB. The system auto-detects the hospital's schema format, generates pipeline configs, maps fields to our clinical model, runs validation, starts processing. No data engineers writing custom ETL. No weeks of integration meetings. This is what scaling looks like: 100 hospitals without 100 engineering teams.
What You See
- 1Feed in a sample data export. Schema auto-detection does its thing.
- 2Fields mapped automatically to THB's clinical data model
- 3Pipeline configs generated with validation rules baked in
- 4Pipeline runs in real time -- watch the progress bar
- 5Data quality scores: completeness, consistency, accuracy. Before you go live.
- 6Before/after: fragmented mess to unified Patient 360
What Happens Underneath
- •Schema detection combines pattern matching with LLM classification of hospital fields
- •150+ known hospital formats already in the mapping library -- chances are, we have seen yours
- •Pipeline configs: YAML-based, version-controlled, hospital-specific. Stored, not thrown away.
- •50+ data quality checks run on every ingestion batch before anything goes live
- •MDM resolves patient identities on the first processing run. Not the third. The first.
WhatsApp Patient AI Assistant
The Story
Priya gets a WhatsApp message from her hospital: 'Hi Priya, your HbA1c test from last week shows 7.2%. Dr. Sharma recommends a follow-up visit. Would you like to book an appointment?' She replies in Hindi: 'Haan, kal subah ka time de do.' The AI assistant -- grounded in Priya's actual clinical data -- books a morning slot with Dr. Sharma, sends prep instructions, logs everything in the hospital CRM. The whole thing happens in WhatsApp, in her language. And the AI never once crosses the line into medical advice it is not authorized to give.
What You See
- 1Live WhatsApp conversation with the AI assistant
- 2Patient switches between Hindi and English mid-conversation. AI follows.
- 3Books an appointment right there -- available slots, confirmation, done
- 4Lab results delivered with clinician-approved messaging templates
- 5Medication refill reminders that connect to the pharmacy
- 6Watch what happens when the patient asks something outside clinical scope. The guardrails fire.
What Happens Underneath
- •LLM grounded in Priya's actual clinical data from NovaHub. Not generic. Not hallucinated.
- •WhatsApp Business API with pre-approved message templates
- •Safety guardrails: topic boundaries, escalation triggers, clinical scope limits. Non-negotiable.
- •Conversation state machine tracks context across messages -- no amnesia between replies
- •Every interaction logged in CRM Suite. The care team sees exactly what happened.
Multi-Hospital Benchmarks
The Story
The regional director of a 5-hospital chain opens their benchmark dashboard. Hospital A: highest follow-up compliance at 78%. What are they doing right? Hospital C: 3.2 care gaps per patient. That needs fixing. Hospital B: patient satisfaction tracks their 24-hour discharge follow-up rate. The director digs in -- Hospital A's edge is a systematic morning brief workflow. One click. That workflow config is replicated to every other hospital in the chain. Same YAML, different tenant. That is how you standardize operations without a 6-month consulting project.
What You See
- 1Side-by-side hospital comparison across 20+ KPIs
- 2Follow-up compliance, care gap density, satisfaction scores -- all comparable
- 3The system identifies what the best hospital does differently
- 4One click: replicate that workflow to every other location
- 5Trend lines showing whether each hospital is actually improving
What Happens Underneath
- •DataCloud normalizes data across hospitals into one unified model. Same definitions, every facility.
- •20+ KPIs computed with identical methodology -- no apples-to-oranges comparisons
- •Workflow configs are YAML-based and hospital-agnostic. Copy, paste, adapt.
- •Replication engine auto-adapts workflow configs to each target hospital's tenant
- •Tenant isolation means data never crosses hospital boundaries. Only aggregated KPIs are shared.
The Complete Picture
Each demo proves a different claim. Together, they prove the platform works end to end.
| Demo | Proves | Audience | Impact | Duration |
|---|---|---|---|---|
| D1: Patient 360 Intelligence View | Data depth + speed | Investors + CXOs | Critical | 4 min |
| D2: AI Clinical Follow-up Engine | Revenue generation | Investors + Clinical | Critical | 5 min |
| D3: Natural Language Analytics | AI-native analytics | Investors + CXOs | Critical | 3 min |
| D4: Care Gap Detection & Closure | Clinical intelligence | Clinical + Investors | High | 4 min |
| D5: Hospital Onboarding in 48 Hours | Scale without engineers | Investors (Scale) | High | 4 min |
| D6: WhatsApp Patient AI Assistant | Patient-facing AI | Investors + Ops | High | 3 min |
| D7: Multi-Hospital Benchmarks | Chain scalability | Chain CXOs | Medium | 3 min |
See It Live
25 minutes. Seven demos. Zero slides. We will walk you through real data flowing through the real platform. Bring your skepticism.