Can you build AI into a healthcare product?

Yes — I have architected clinical decision support, AI meal analysis and LLM health assistants on a custom model + vector database, with the privacy and reliability healthcare demands.

Can you build AI into a healthcare product?

Yes — I have architected clinical decision support, AI meal analysis and LLM health assistants on a custom model + vector database, with the privacy and reliability healthcare demands.

AI consulting & engineering

Implementing AI in healthcare products

Q: How do you actually implement AI in a business?

I find where AI creates real value, design the architecture, and build it end-to-end — running systems, not slideware.

Q: Can you build a production mobile app in React Native?

Yes — I ship native iOS and Android apps from one Expo/React Native codebase, with payments, provisioning, offline-first flows and clean native integrations.

Q: How do you scale a Node.js backend?

With the right architecture: services split by load, event streaming, queues and caching, observability, and databases chosen per workload — proven on a platform that grew to 3M+ users.

Q: Can you build an MVP and then scale it?

Yes — I build MVPs end-to-end fast, then evolve the same codebase to production scale instead of throwing it away. Several of mine grew to acquisition or millions of users.

Q: How do you build a project with agentic AI development?

I drive AI coding agents (Claude Code, Codex, Cursor) with custom rules, hooks and context, spec-first — then gate everything with tests and AI review so speed never costs quality.

Q: How do you use CodeRabbit for code review and quality?

CodeRabbit reviews every pull request automatically — catching bugs, security issues and smells — as a gate alongside human review and a real test suite, so nothing weak reaches main.

Q: Can you build a custom AI model for my product?

Yes — self-hosted models on Ollama (Llama, Mistral, DeepSeek), fine-tuned or RAG-grounded, when you need privacy, control or a task an off-the-shelf API can’t do well.

Q: How do you build a RAG system?

Chunk and embed your data into a vector database, retrieve and rerank the right context per query, ground the model in it, and evaluate — so answers are accurate and cite your sources, not hallucinations.

Q: Which AI model should you use — Claude, GPT, Gemini, Perplexity or Grok?

It depends on the task — and the real answer is usually more than one. I pick and route across frontier models by strength, with evals and fallbacks, so you get the best result without lock-in.

Healthcare is where AI has to be right, private and explainable. I have designed and built AI into health products end-to-end — from a model that helps doctors make treatment decisions to AI meal analysis from a photo — with the reliability and data-handling this domain demands.

Clinical decision support that clinicians trust

On Ealthiness I architected a custom AI model on top of Ollama with a Weaviate vector database to help doctors make treatment decisions, alongside an LLM trainer chat (Gemini/OpenAI with fallback) and AI meal analysis from photos. Self-hosting the core model keeps sensitive data under control, and fallbacks keep the assistant available when a single provider fails.

Built for privacy and scale

Health platforms carry real regulatory and reliability weight. I have paired NestJS backends with MinIO storage, Redis and BullMQ queues, and self-hosted inference so patient data stays where it should. Earlier I built mDoc.one for one of Germany’s leading digital-health companies and a US post-surgery patient-tracking platform — so I know how these systems fail and how to keep them up.

From mobile app to admin to model

A health product is more than a model: it is a patient app, a clinician/admin surface and the AI in between. I design all three together — Expo/React Native apps, React admin panels and the AI services behind them — so the experience is coherent and the data flows cleanly from capture to insight.

Key points

Clinical decision support on a self-hosted model + vector DB keeps sensitive data controlled.
Provider fallbacks and queues keep health AI available and reliable.
Whole-product thinking: patient app, clinician admin and the AI between them.

Tools I reach for

OllamaWeaviateGeminiOpenAINestJSRedisBullMQMinIOExpo

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Implementing AI in healthcare products

Clinical decision support that clinicians trust

Built for privacy and scale

From mobile app to admin to model

Related work

Have something like this in mind?

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