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Anesthetize.ai  ·  Medical Education / AI

Zero-Hallucination
AI Platform

How we built a medical education chatbot with complete source verification, RAG-grounded responses, and zero tolerance for AI confabulation — live in 10 weeks.

15,000+
Active Users
in 3 Months
<2s
Avg Response
Time
97%
User
Satisfaction
99.95%
API
Uptime
10 wks
Kickoff
to Launch
01
01
The Challenge

The Problem

Anesthetize.ai needed a reliable AI chatbot for medical education — with zero hallucinations. Inaccurate medical information could have serious consequences for students and practitioners relying on the platform.

"This wasn't a chatbot that could guess wrong and have someone refresh the page. Every answer had to be defensibly accurate."

They needed:

  • A web application accessible on all modern browsers
  • iOS and Android mobile apps with full feature parity
  • A backend API with strict accuracy requirements and source verification
  • Scalable architecture for a rapidly growing user base
  • Enterprise-grade licensing and user management
02
02
Our Approach

How We Solved It

We designed a three-tier architecture covering every touchpoint:

1

API Server (.NET)

  • RESTful API with strict prompt engineering
  • Retrieval-Augmented Generation (RAG) pipeline using OpenAI embeddings over curated medical knowledge bases
  • Real-time web search integration with cited references
  • User authentication, session management, and enterprise licensing
  • Caching layer for performance
2

Web Application (React / Next.js)

  • Responsive interface accessible on all devices
  • Real-time chat with streaming responses
  • Inline source citations for every AI response
  • Enterprise user management dashboard
3

Mobile Apps (React Native)

  • Feature-parity with the web application
  • Published to both the App Store and Google Play
  • Secure authentication, offline-friendly architecture
0

Zero-Hallucination Strategy

The core technical challenge was preventing AI from making things up. Every layer of the system was designed to resist confabulation:

RAG Pipeline: Every response grounded in verified medical literature using OpenAI embeddings and semantic search
Web Search Integration: Real-time search with cited references to supplement the knowledge base
Source Attribution: Every response includes citations — no anonymous claims
Confidence Scoring: If uncertain, the system acknowledges its limitations rather than guessing
Automated Testing: Continuous testing against known medical facts before every release
03
03
Deliverables

What We Delivered

API Server
Fully documented REST API, 99.95% uptime SLA, built on .NET
Web App
Responsive React / Next.js app, fast and accessible on all modern browsers
iOS App
Full-featured app published to the App Store
Android App
Full-featured app published to the Google Play Store
Enterprise Management
Licensing controls, user roles, and permissions for institutional deployments
Documentation
API docs, user guides, admin manuals
8 Post-Launch Features
Major features added after launch with minimal disruption to existing users
04
04
Results

The Results

Metric
Result
Hallucinations in production (6 months)
Zero
Active users (3 months post-launch)
15,000+
Average response time
< 2 seconds
User satisfaction score
97%
API uptime
99.95%
Post-launch features added
8

Business Impact

  • Client secured Series A funding, citing platform reliability as a key factor
  • Expanded to 3 additional medical specialties
  • Enterprise licensing deals signed with 5 medical schools

"The team delivered exactly what we needed, when we needed it. More importantly, they understood the criticality of accuracy in our domain. The architecture they built has supported us through rapid growth and feature expansion. This project set the foundation for our entire business."

Client, Anesthetize.ai
Quote attribution pending client confirmation
Tech Stack

Technologies Used

.NET React / Next.js React Native TypeScript OpenAI API RAG / Vector Search iOS Android CI/CD Cloud Hosting
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