🏥 Rogsutra
Healthcare AI Chatbot & Symptom Advisor
Who We Built This For
Rogsutra was built for a network of multi-specialty clinics looking to extend their reach beyond physical hours. Patients increasingly turned to the internet for symptom research before calling a doctor — the clinics needed a medically reliable, AI-powered first point of contact that could triage patients, provide guided health information, and route them to the right specialist without being misleading or dangerous.
What We Set Out to Build
Rogsutra is an AI-powered healthcare chatbot that acts as a patient's first point of contact — available 24/7 to assess symptoms, provide evidence-based health guidance, and connect users to the right specialist. Built on a fine-tuned NLP model trained on clinical datasets, it understands natural language symptom descriptions and maps them to likely conditions with confidence scores.
The chatbot doesn't replace doctors — it routes patients intelligently. A patient describing fatigue, increased thirst, and frequent urination gets a structured symptom report, a potential match to diabetes-related conditions, and a recommendation to book an endocrinologist — all within seconds, at 2am.
For the clinic network, the result was a dramatic reduction in trivial walk-ins, a better-prepared patient cohort for actual appointments, and a digital engagement layer that kept the clinic present in patients' lives between visits.
Project Facts
Tech Stack
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Start a ProjectWhat Needed to Be Solved
Patients with minor ailments flooded clinic queues while genuinely urgent cases waited. After-hours inquiries went unanswered. The clinic had no digital triage layer — every question, trivial or critical, required a human response during business hours only.
After-hours patient queries went completely unanswered — zero digital support outside clinic hours
Doctors wasted 30–40% of appointment time on cases patients could have self-managed
No structured triage meant urgent and trivial cases entered the same queue
Patients turned to unreliable internet searches instead of medically guided information
How We Tackled It
Medical Dataset Curation
Sourced and cleaned symptom-condition datasets from clinical knowledge bases; validated training data accuracy with practising doctors.
NLP Model Training
Fine-tuned a transformer-based model for symptom extraction, condition matching, and intent classification from free-text patient input.
Chatbot Flow Design
Designed conversational flows covering 200+ symptom clusters with escalation triggers for urgent red-flag symptoms.
Doctor & Booking Integration
Integrated chatbot recommendations with the clinic specialist directory and appointment booking system for seamless AI-to-human handoff.
Web + App Launch
Deployed on clinic website and as a React Native app. Monitored chatbot accuracy against doctor feedback for first 90 days.
What We Built
AI Symptom Analysis
NLP-powered assessment understands free-text symptom descriptions and maps to 200+ conditions with confidence scoring.
Smart Doctor Routing
Automatically recommends the right specialist based on symptom analysis — reduces misdirected bookings.
Health Summary Reports
Generates structured symptom summaries patients can share directly with their doctor at appointment time.
Urgent Symptom Escalation
Detects red-flag symptoms (chest pain, stroke indicators) and immediately prompts emergency action.
Telemedicine Booking
Direct appointment booking integrated into chatbot flow — patients go from symptoms to booked slot in under 2 minutes.
Multilingual Support
Supports Hindi and English with dialect-aware NLP — accessible to patients across India regardless of language preference.
Results That Speak for Themselves
Full Scope of Work
Trained an NLP model on curated clinical symptom datasets validated by practising doctors
Built conversational flows covering 200+ symptom clusters with emergency escalation for red-flag conditions
Integrated with specialist directory and appointment booking for seamless patient handoff from AI to doctor
Generated structured symptom summary reports patients can share at consultation
Deployed on web and mobile with real-time monitoring and doctor feedback loop for ongoing accuracy improvement
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Project timelines vary by complexity. Most of the platforms in our case studies were delivered in 3–6 months from initial requirements to production launch, including testing and client feedback cycles.
Yes. We follow an Agile methodology with 2-week sprints. Clients receive working demos every sprint and provide feedback that directly shapes the next iteration. There are no surprises at launch.
Absolutely. Every project we take on starts with a free consultation where we understand your specific requirements and create a tailored scope. Use the "Start a Project" button on this page to connect with us.
Yes. You receive the complete source code, deployment documentation, API documentation, and a handover session with our team. You'll be able to manage and extend the system independently or with any developer.
All projects include a 30–60 day post-launch support period at no extra cost. After that, we offer monthly retainer maintenance packages for updates, performance monitoring, security patches, and new feature development.
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