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Industries · Healthcare & Digital Health

Health AI that knows its limits.

In health, an AI that is confidently wrong is worse than none. I build assistants and models that are useful and cautious: grounded guidance, risk detection that routes people to real help, and screening models designed as decision support, not diagnosis.

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Where AI pays off

What I build for teams like yours.

  • 01

    Patient and member support

    24/7 conversational support grounded in approved material.

  • 02

    Risk-aware assistants

    Automatic detection of crisis signals with escalation to helplines or staff.

  • 03

    Medical image screening

    CNN classifiers that help prioritize scans for clinical review.

  • 04

    Voice and WhatsApp access

    Support where patients already are, including voice notes.

What makes it hard

The constraints are the project.

The model is rarely the problem. These are the requirements that decide whether a system ships, and they are designed in from the first week.

  • Safety first

    Escalation paths and guardrails tested as thoroughly as the happy path.

  • Decision support, not diagnosis

    Models positioned to assist clinicians, with honest evaluation.

  • Sensitive data

    Minimal data retention, anonymization and least-privilege access.

  • Reliability

    Automated test suites covering conversations, sessions and security.

Proof

Relevant case studies.

Digital Health4 stages
  1. 01Screen
  2. 02Retrieve
  3. 03Generate
  4. 04Remember

Stage 1, Screen: Crisis-risk detection. Stage 2, Retrieve: Pinecone CBT/mindfulness corpus. Stage 3, Generate: GPT-4o via LangChain. Stage 4, Remember: Session memory and mood history.

01Namhee

Safety-Aware Mental-Health AI Assistant

A RAG-grounded mental-health support assistant with mood assessment and tracking, CBT and mindfulness guidance, session memory and automatic crisis-risk detection that points users to helplines.

Pass rate across 11 core tests
100%
RAG response target
<5s
Crisis-risk escalation
Auto
Read case study
Healthcare5 stages
  1. 01Scan
  2. 02Prepare
  3. 03Backbone
  4. 04Classify
  5. 05Evaluate

Stage 1, Scan: Medical image intake. Stage 2, Prepare: Image preprocessing. Stage 3, Backbone: Pretrained CNN (transfer learning). Stage 4, Classify: Tumor and retinopathy heads. Stage 5, Evaluate: Held-out validation.

02Medical Imaging AI

Medical Imaging Diagnostics with Computer Vision

CNN classifiers for brain-tumor detection and diabetic-retinopathy screening, built with transfer learning in TensorFlow as decision support for faster triage of medical scans.

Diagnostic screening models
2
Transfer-learning architecture
CNN
Training framework
TensorFlow
Read case study
whatsapp · ai-sponsor
24/7
0:14

whisper → “Tonight is hard. Can we talk for a minute?”

0:19

I’m here. Let’s take it one step at a time. What’s on your mind?

prompt cache · input tokens≈90% saved

Illustrative interface · client data never shown
03AI Sponsor

Multi-Channel AI Support SaaS Platform

The backend for a 24/7 AI sponsor that supports people in 12-step recovery over chat and WhatsApp voice. It combines Claude-powered conversations, Stripe subscription billing and a prompt-caching architecture that cut LLM input costs by about 90%.

LLM input-cost reduction
~90%
Chat and voice availability
24/7
Automated test categories
12
Read case study

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FAQ

Healthcare & Digital Health: common questions.

How do you make health chatbots safe?

With a dedicated risk-detection layer that screens messages and escalates to crisis resources, grounded content instead of open-ended generation, and test suites that cover the safety paths explicitly.

Can AI diagnose from medical images?

The models I build are screening and prioritization tools that support clinicians. They're evaluated honestly on held-out data and are not a substitute for clinical diagnosis.

How is patient data protected?

Data minimization, anonymization where possible, deployment in your environment and strict access control, with retention matched to your policies.

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