Four Platform Layers. One Pipeline. Data Becomes Action.
DataCloud processes the mess. NovaHub serves it in real time to any application. Application Engine gives it structure. AI Platform makes it act. We built all four because no one else would -- and bolting vendors together doesn't work.
Watch clinical data flow from raw hospital systems to actionable patient engagement
The Complete Architecture
Seven layers, each independently valuable. But the real advantage is vertical integration -- data flows through the entire stack without translation layers or sync jobs.
The Pipeline IS the Product
Follow a single patient record from raw hospital export to booked follow-up appointment. Every step below is automated.
Ingest
150+ formatsRaw data arrives in 150+ formats -- CSV dumps, HL7 messages, proprietary exports. Schema auto-detection figures out the structure. No custom code per hospital.
Clean & Structure
ELT + MDMRaw data gets cleaned, renamed, deduplicated, and structured into dimensions and fact tables. MDM resolves patient identities across systems -- 3 fragmented records become 1 golden patient. The ELT layer does the heavy lifting so downstream queries are fast.
Enrich
200+ care gapsNow the data gets smart. Clinical flags, risk scores, care gap detection against 200+ protocol definitions. Cohort builder segments patients. Journey assembler stitches timelines together.
Serve
Real-Time APIsNovaHub delivers curated patient data to any application -- CRM, HIS, EMR, AI agents. Phone number to complete clinical profile, in real time, via config-driven APIs.
Engage
33% conversionPEP generates personalized follow-up messages and runs care gap campaigns. CRM Suite handles tickets, leads, and agent workspace. WhatsApp, SMS, Email, Voice -- the patient's preferred channel.
Measure
5.3x ROIEvery message, every call, every appointment -- attributed back to revenue. You know exactly which campaign, which channel, which care gap generated the booking.
Platform Layers. Deep Dive.
Each layer works on its own. But the compound effect of vertical integration is why competitors can't just copy one piece.
DataCloud
The Data Engineering Layer
The hard problem nobody wants to solve. 150+ hospital data formats, each with its own quirks. DataCloud ingests them all, resolves patient identities into golden records, computes clinical flags, builds cohorts, detects care gaps, and assembles patient journeys. All config-driven -- no custom code per hospital.
Explore DataCloud- Schema auto-detection from raw hospital exports
- ELT: clean, rename, deduplicate, type-cast, drop columns
- MDM: 3 records become 1 golden patient via exact-match-first
- Dimension & fact table build: star schema, surrogate keys
- Metrics engine: clinical flags, risk scores, pre-computed aggregations
- Cohort builder + care gap detection: 200+ protocol definitions
NovaHub
Real-Time Data for Every Application
Your CRM, HIS, EMR, and AI agents all need curated patient data in real time. NovaHub delivers it. Register a dataset in DataCloud, get an API endpoint automatically. No custom code, no integration projects. Curated, resolved, intelligence-ready data for any application.
Explore NovaHub- Arrow Flight: columnar data streaming at wire speed
- Purpose-built storage engine for high-throughput clinical data
- Identity resolution: phone number to unified patient record
- Multi-dimension filtering across clinical datasets
- Atomic updates: swap data versions with zero downtime
- Config-driven: register a dataset, get an API -- no code changes
Application Engine
The Schema-Driven Backend
We got tired of writing CRUD. So we made it so that defining an entity schema generates the API endpoints, validation rules, UI components, and AI tools automatically. Every hospital gets the same engine with schema-per-tenant isolation. No forks.
Explore Application Engine- Schema-driven: entity definitions generate everything
- Workflow engine: visual builder for clinical automation
- Event system: triggers on create, update, delete, schedule
- Multi-tenant: complete data isolation per hospital
- AI agent integration: tools auto-generated from schemas
- Preset system: deploy entire CRMs from config
PEP
Patient Engagement Platform
Marketing automation that actually works in healthcare. Cohort-based segmentation, multi-channel journeys, care gap campaigns, and full-funnel ROI attribution -- from patient identification to booked appointment to revenue.
Explore PEP- NL-powered cohort builder with clinical dimensions
- Visual journey builder with branching and scheduling
- 4-channel delivery: WhatsApp, SMS, Email, Voice AI
- 200+ care gap definitions with automated closure campaigns
- Morning intelligence brief: prioritized daily action plan
- Full-funnel ROI attribution: per-campaign, per-channel, per-condition
CRM Suite
Operational CRM for Healthcare
CRM presets for any healthcare vertical -- Patient, Doctor, B2B, Sales, or build your own. Deployed from config, not custom code. Tickets, leads, tasks, SLAs, telephony, AI agents, Patient 360 workspace. One codebase. Different schemas per hospital.
Explore CRM Suite- Preset-driven deployment: any CRM vertical from configuration
- Specialist AI agents with coordinator routing
- Metadata-driven UI: auto-generated forms, tables, views
- Tickets, leads, tasks with SLA tracking and escalation
- Telephony integration: click-to-call, screen pop, call outcomes
- Knowledge bank with AI agent assist for live call support
AI Platform
Runtime Kernel & Agent Framework
Every AI interaction -- chat, voice, WhatsApp, autonomous background jobs -- runs through the same runtime with the same safety guardrails. Pluggable LLM backend, auto-generated tools, trust and safety enforcement. The LLM proposes, the policy layer decides, the executor enforces.
Explore AI Platform- Unified execution across all deployment modes
- Policy engine: LLM proposes, policy decides, executor enforces
- Trust & safety: injection protection, toxicity filtering, topic boundaries
- Pluggable LLM: swap models without changing agents
- Agent builder: create and deploy agents without code
- Intent routing, conversation memory, cost budgets
PrismML
ML Intelligence Layer — DataCloud Add-on
Hospital-localized ML training and batch inference on DataCloud data. Models are trained on your patient population, scored nightly, and written back to the data platform — enriching every patient profile, care gap, and campaign with ML intelligence. Governed by promotion gates, drift monitoring, and fairness analysis.
Explore PrismML- Experimentation: multi-framework model selection and tuning
- Training + inference: DataCloud-native, nightly population scoring
- Promotion gates: 3-gate governance before production deployment
- Drift monitoring: feature/score drift with auto-retraining
- Explainability + fairness: SHAP, calibration, demographic parity
- Closed loop: ML scores enrich Patient360, cohorts, and campaigns
Why We Built It This Way
Not another healthcare dashboard. Not a horizontal CRM with a hospital skin. We made specific architectural bets -- and they paid off.
Configuration, Not Customization
Every hospital gets the same platform. Differences live in config -- schemas, workflows, agents, reports. No fork. No custom branch. We refused to build a services company disguised as a product company.
Show the Data Flowing, Not the Screens
Dashboards don't treat patients. We show data flowing -- from hospital systems through clinical intelligence to patient follow-ups. If you can't trace the pipeline, you're just looking at screenshots.
AI-Native, Not AI-Washed
AI is in the architecture, not bolted on for the pitch deck. Agent framework, toolset registry, typed state actions, strict validation. Every AI interaction is testable and auditable. That matters when you're dealing with patient data.
Healthcare-Specific, Not Horizontal
200+ care gap definitions. Clinical flags. Hospital presets. Hindi/English multilingual. Safety guardrails that actually stop the AI from diagnosing. Built for Indian healthcare from day one -- not a generic SaaS with a hospital skin.
Hospital Onboarding: 48 Hours, Not 48 Weeks
Traditional healthcare CRM vendors quote 3-6 months per hospital. We quote 48 hours. Here is how.
Connect
Hour 0-4
Schema auto-detection reads the hospital's data exports. Formats identified, fields mapped, pipeline config generated. Most columns auto-detected.
Process
Hour 4-12
MDM resolves patient identities. Clinical flags computed. Care gaps detected. Cohorts built. Journeys assembled.
Configure
Hour 12-24
PEP and CRM Suite configured with hospital-specific entities, workflows, and AI agents. Multi-channel communication set up.
Live
Hour 24-48
Morning brief lands in staff inboxes. First AI-drafted patient messages go out. ROI tracking starts. The hospital is live and seeing value.
Numbers. With Context.
Data Engineering
Data Delivery
Patient Outcomes
See It Running. 25 Minutes. No Slides.
7 demos. Real hospital data flowing through real infrastructure to real patient engagement and measured ROI.