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Zero-Hallucination AI Platform

We built a medical education chatbot that 15,000+ healthcare professionals trust for accurate, citation-backed answers — with zero hallucinations in six months of production use.

Client: Anesthetize.ai • VectrForce Technology Studio
15,000+Active Users in 3 Months
<2sAvg Response Time
97%User Satisfaction
99.95%API Uptime
10 wksKickoff to Launch
01
Chapter

The Challenge

Anesthetize.ai needed an AI chatbot for medical education with a non-negotiable requirement: zero hallucinations. A single inaccurate answer in a clinical training context could erode trust with an entire institution and put the startup's reputation at risk.

Beyond accuracy, the client needed a full product suite shipped fast:

Production web application
Native iOS and Android apps
Scalable backend API server
Enterprise licensing and admin
Scalable cloud architecture
02
Chapter

Our Approach

We designed a three-tier architecture that separated concerns cleanly and let each layer scale independently.

Tier 1

API Server (.NET)

  • RAG pipeline with vector search
  • Real-time web search integration
  • JWT authentication and RBAC
  • Streaming response delivery
  • Usage analytics and rate limiting
Tier 2

Web App (React / Next.js)

  • Real-time streaming chat UI
  • Inline citations with source links
  • Conversation history and search
  • Responsive design for all devices
  • Dark/light theme support
Tier 3

Mobile Apps (React Native)

  • iOS and Android from shared codebase
  • Push notifications for study reminders
  • Offline answer caching
  • Biometric auth integration
  • App Store and Play Store deployment
Zero-Hallucination Strategy
01
RAG PipelineRetrieval-augmented generation grounded in curated medical textbooks and peer-reviewed sources.
02
Web Search IntegrationReal-time web search for the latest clinical guidelines and drug interactions.
03
Source AttributionEvery response includes inline citations that link back to the original material.
04
Confidence ScoringResponses below the confidence threshold are flagged and routed for human review.
05
Automated Testing500+ adversarial prompts run nightly to catch regressions before users do.
03
Chapter

What We Delivered

01API Server (.NET) with RAG pipeline, auth, and streaming
02Web Application (React / Next.js) with real-time streaming and citation UI
03iOS App (React Native) with push notifications and offline caching
04Android App (React Native) with feature parity to iOS
05Enterprise Management dashboard for licensing, analytics, and user admin
06Full technical and API documentation
078 Post-Launch Features shipped in the first 3 months
04
Chapter

The Results

MetricResult
Hallucinations in ProductionZero
Active Users15,000+
Average Response Time<2s
User Satisfaction97%
API Uptime99.95%
Post-Launch Features8
Business Impact
Series A funding secured on the strength of the platform
Expanded into 3 additional medical specialties
Closed 5 medical school enterprise licensing deals
“
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.
Anesthetize.ai — Founding Team
Full Tech Stack
.NETReact / Next.jsReact NativeTypeScriptOpenAI APIRAG / Vector SearchiOSAndroidCI/CDCloud Hosting
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