Built a patient-facing AI assistant with RAG-grounded responses, adversarial safety testing, and HIPAA-compliant architecture — zero hallucinated medical advice in 200,000+ interactions.
Client
Regional Healthcare Network
0%
Hallucination Rate
78%
Containment Rate
99.2%
Response Accuracy
3/3
Compliance Audits
A 14-hospital healthcare network wanted an AI assistant to handle patient inquiries — appointment scheduling, prescription refill status, insurance eligibility checks, and general health information. But after studying the Google Gemini incident (where the AI told a student to die), Air Canada's chatbot inventing refund policies, and the RAND Corporation data showing 80% of corporate AI projects fail, their compliance team was understandably terrified. The stakes were different here: a hallucinated medical recommendation could cause real patient harm. Their requirements were non-negotiable — zero tolerance for fabricated medical advice, full HIPAA compliance, complete audit trails, and graceful human handoff when the AI reached its knowledge boundary. Previous vendor proposals had either been generic chatbot platforms with no healthcare-specific safety controls, or enterprise solutions quoting ₹3Cr+ with 18-month timelines.
We built a RAG-grounded AI assistant that could only answer from verified, hospital-approved knowledge sources — never from the LLM's general training data. The architecture had three layers: a retrieval layer using a vector database indexed with 12,000+ hospital-approved documents (formularies, scheduling policies, insurance guidelines, facility information), a generation layer using Claude Opus 4.6 with strict system prompts that prohibited medical diagnosis or treatment recommendations, and a safety layer with real-time output validation that checked every response against a medical claims classifier before it reached the patient. We implemented seven categories of adversarial testing before launch: prompt injection attempts, jailbreak sequences, requests for diagnosis, medication dosage queries, mental health crisis detection, insurance misinformation probes, and politically sensitive health topics. The crisis detection system was trained to recognize signs of self-harm or medical emergencies and immediately route to a live nurse. Every interaction was logged with full audit trails for HIPAA compliance, and the system operated within a SOC 2-compliant infrastructure with encrypted data at rest and in transit.
0%
Hallucination Rate
Zero fabricated medical claims in 200K+ interactions
78%
Containment Rate
Patient inquiries resolved without human intervention
99.2%
Response Accuracy
Verified against hospital-approved knowledge base
3/3
Compliance Audits
Passed all HIPAA compliance audits
Deployed a secure GenAI platform enabling governed AI adoption without risking data leakage, improving operational efficiency across enterprise.
Built an AI-powered design hub that optimized country and site selection, accelerating trial start-up and reducing operational costs significantly.
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