Sanjeevani.ai

Inspiration Thalassemia patients in India endure constant anxiety over securing compatible blood, especially in remote regions. Fragmented donor networks, unpredictable supply shortages, and low disease awareness inspired a unified, technology-driven platform to bridge patients and donors—transforming blood access from a desperate scramble into a reliable service.

What It Does Sanjeevani.ai leverages AI to match patients with eligible donors in real time, forecasts regional blood shortages, and visualizes donor–patient clusters on interactive maps. It delivers SMS alerts, a multilingual chatbot, and targeted awareness content. A unified dashboard connects hospitals, NGOs, and national registries (e-RaktKosh, Blood Bridge), ensuring seamless data flow and proactive donor engagement.

How We Built It Backend & AI: Deployed Python-based ML models (TensorFlow) for compatibility scoring and shortage prediction.

Frontend & SMS: Created a React Native app alongside an SMS gateway integration for feature-phone reach.

Mapping & Integration: Implemented PostGIS for geospatial clustering and built mock APIs for future e-RaktKosh/Blood Bridge integration.

Content & Chatbot: Developed region-specific educational modules and a Rasa chatbot offering 24/7 multilingual support.

Challenges We Ran Into Data Gaps: Limited access to real-world transfusion records required us to simulate initial datasets.

Connectivity Variability: Ensuring low-latency SMS and app notifications across low-bandwidth areas proved complex.

API Uncertainty: Lack of stable, documented endpoints forced the use of mock services and manual data sync prototypes.

Accomplishments We’re Proud Of Built a working predictive analytics prototype for donor–patient demand forecasting.

Launched an SMS-driven registration and alert system serving both smartphones and feature phones.

Designed an interactive geospatial dashboard illustrating real-time donor availability and hospital demand clusters.

Curated the first set of multilingual awareness campaigns integrated directly into the app and chatbot.

What We Learned Real-world healthcare data often requires extensive cleaning and validation before modeling.

Multi-channel communication (SMS, app, chatbot) is critical for inclusive reach in diverse connectivity landscapes.

Early stakeholder engagement (hospitals, NGOs, donors) is essential to refine workflows and integration requirements.

What’s Next for Sanjeevani.ai Integrate with e-RaktKosh and Blood Bridge for live data exchange and seamless record updates.

Conduct field pilots with partner blood banks to validate cold-chain tracking and improve logistics.

Expand predictive models using real transfusion and donor history to refine accuracy.

Launch region-specific awareness drives in local languages, measure impact, and iteratively optimize content.

Pursue regulatory approvals and ethical certifications to support nationwide scaling and long-term sustainability.

Built With

• ai-matching-&-analytics:-tensorflow
• airflow-security-&-privacy:-oauth-2.0
• and-airflow.-to-ensure-security-&-privacy
• and-consent-management-systems.-in-education-&-outreach
• and-firebase-cloud-messaging.-for-database-&-integration
• and-multilingual-apis.-for-dashboards-&-visualization
• and-postgis.-to-enable-real-time-&-mobile-access
• and-power-bi.-to-support-iot-&-geographic-mapping-at-scale
• consent-management-education-&-outreach:-strapi
• contentful
• dialogflow
• encryption-tools
• firebase-cloud-messaging-database-&-integration:-postgresql
• flutter
• geodjango
• google-maps
• kong
• mapbox
• mongodb
• multilingual-apis-dashboards-&-visualization:-dash
• our-stack-includes-postgresql
• postgis-real-time-&-mobile-access:-react-native
• power-bi-iot-&-geographic-mapping-(scaling):-rfid/nfc-kits
• pwa
• pytorch
• rest/graphql-apis
• scikit-learn
• tableau
• we-plan-to-build-with-react-native
• we-will-implement-oauth-2.0
• we-will-integrate-rfid/nfc-kits
• we-will-use-technologies-like-tensorflow
• we-will-use-tools-like-dash
• we?ll-leverage-strapi