Healthcare Production AI Clinical NLP

iYara Health: AI That Works
In the Real World

Building clinically-meaningful AI for healthcare delivery in West Africa. Not chatbots — real clinical intelligence that handles hallucinations, multi-language NLP, and offline scenarios.

5
AI Systems
3
Languages
GDPR
HDS Compliant
Edge
AI Inference
Production AI

Not Just Another Chatbot

iYara's AI handles the hard problems that tutorials don't teach: clinical hallucinations where wrong answers can harm patients, multi-language NLP in French and local languages, and edge inference when connectivity fails.

Medical knowledge graph for grounded reasoning
Context window management for long consultations
Latency optimization for real-time transcription
Offline-capable models for variable connectivity
Confidentiality Notice: Certain sensitive information including specific AI model configurations, vendor names, and infrastructure details has been withheld. iYara has reviewed and validated all information presented here.
The Problem

Healthcare AI That Actually Works

Most "AI healthcare" solutions are glorified chatbots. They work in demos, fail in production. The West African healthcare context makes this worse: variable connectivity, multiple languages, limited documentation, and patients who can't afford wrong answers.

iYara needed AI that could handle real clinical workflows — not just answer questions, but reason about symptoms, transcribe consultations in real-time, generate accurate medical notes, and predict no-shows. All while maintaining GDPR/HDS compliance and working on 3G networks.

The challenge wasn't just technical. It was building AI systems where hallucinations can harm patients, where context windows must handle hour-long consultations, and where latency during transcription breaks the doctor-patient flow.

Hallucination risk

Wrong AI answers can harm patients

Multi-language NLP

French + local languages in clinical context

Real-time latency

Transcription can't lag behind speech

Offline requirement

Must work on unreliable connectivity

AI Architecture

Five Production AI Systems

Not one chatbot — five distinct AI systems, each solving different clinical problems

Intelligent Triage

Natural language symptom intake with medical knowledge graph-powered reasoning. Urgency classification (Emergency, Urgent, Routine) with provider specialty matching.

NLP Knowledge Graph Classification

Ambient Clinical Intelligence

Real-time consultation transcription with automatic medical note generation, ICD coding suggestions, and drug interaction alerts during prescribing.

ASR LLM ICD-10

Predictive Health Insights

Pattern recognition across patient history. Appointment no-show prediction, chronic disease monitoring alerts, and population health analytics for providers.

ML Time Series Analytics

Voice-Enabled Accessibility

Voice input for low-literacy users, audio playback of instructions. Multi-language ASR supporting French and local languages.

Speech-to-Text TTS Multilingual

Edge AI Inference

Critical for Africa

When connectivity fails, AI doesn't. Lightweight models run directly on device for core triage functionality. Automatic sync when online, with conflict resolution for data created offline. Priority queue ensures critical patient data syncs first.

3G
Min connectivity
100%
Offline triage
Auto
Sync on reconnect
The Hard Part

Handling Hallucinations in Healthcare

When a chatbot hallucinates about restaurant hours, it's annoying. When clinical AI hallucinates about symptoms, it's dangerous. Here's how we solved it.

The Problem

  • LLMs confidently make up medical information
  • Generic models don't know local disease prevalence
  • Drug interactions require current, accurate data
  • Context windows lose important history in long consultations

Our Solution

  • Medical knowledge graph grounds all reasoning in verified facts
  • RAG pipeline retrieves relevant context before generating
  • Confidence scoring flags uncertain outputs for human review
  • Hierarchical summarization preserves context across long sessions

The Anti-Hallucination Pipeline

1

Input

Symptom description in French or local language

2

Retrieval

Query medical knowledge graph + patient history

3

Generation

LLM generates with grounded context only

4

Validation

Confidence check + citation to source facts

The Platform

Beyond AI: The Full Ecosystem

AI is powerful, but it's just one piece. Here's the complete healthcare delivery platform.

Intelligent Booking

Real-time availability, AI-powered specialty matching, SMS confirmations for non-smartphone users.

Teleconsultation

WebRTC video with adaptive quality for 3G. End-to-end encryption. AI-assisted consultation notes.

Clinic Management

Calendar, patient records, inventory tracking, revenue analytics, staff permissions.

Pharmacy Network

Real-time inventory visibility, prescription routing, drug interaction warnings, generic alternatives.

Escrow Payments

Payment held until service completion. Mobile money + cards + cash points. Dispute resolution.

Compliance

GDPR + APDP (Benin) + HDS (France). End-to-end encryption. Patient consent management. Right to erasure.

Technology

The Stack

AI & ML Stack

LLM APIs RAG Pipeline Vector DB Knowledge Graph ASR/TTS Edge Models

Backend

Node.js TypeScript PostgreSQL Redis WebRTC REST + GraphQL

Mobile & Frontend

PWA (Primary) React Offline-First IndexedDB

PWA chosen for maximum accessibility in low-connectivity regions — no app store required.

Security & Compliance

HDS Certified Hosting E2E Encryption GDPR APDP (Benin)
Key Insight

What I Learned

" Production AI isn't about having the best model — it's about knowing when NOT to use AI. Every system we built has explicit fallback paths to human review. The goal isn't automation for its own sake; it's improving patient outcomes. Sometimes that means the AI says "I don't know, let me get a doctor." "

Building AI for Healthcare or Other High-Stakes Domains?

I've shipped production AI that handles hallucinations, latency, and offline scenarios. Let's talk about your project.

Let's Talk About Your AI Project
Confidentiality Notice: Certain sensitive information including AI model configurations, vendor names, specific accuracy metrics, and infrastructure details has been withheld for security and confidentiality reasons. iYara has reviewed, approved, and validated all information presented in this case study for public disclosure.
Available now

Let's Build Something That Ships

Free 30-minute strategy call. No pitch deck. No sales pressure. Just an honest conversation about your project.

Faster
50%
Less cost
100%
Satisfaction
No commitment
24h response
Free discovery call

Trusted by startups, scale-ups, and enterprises across France and internationally.