Bespoke Healthcare AI. From Data to Production.
Zabrizon builds custom AI solutions for healthcare organisations that can't be solved with off-the-shelf products — healthcare-specific LLMs, clinical NLP pipelines, medical imaging models, and intelligent automation — delivered production-ready with full MLOps infrastructure.
Custom AI Development Services
Full-cycle AI engineering from problem framing through production deployment and monitoring.
Healthcare LLM Fine-Tuning & RAG Systems
Domain-adapted large language models and retrieval-augmented generation systems trained on clinical corpora — for documentation, coding, Q&A, and knowledge retrieval.
- LLM fine-tuning on clinical notes, guidelines, and coding manuals
- RAG architecture over EHR, policy, and knowledge base documents
- Hallucination mitigation and clinical accuracy benchmarking
- HIPAA-compliant LLM deployment on private infrastructure
Computer Vision & Medical Imaging AI
Deep learning models for medical image classification, segmentation, and anomaly detection — across radiology, pathology, dermatology, and ophthalmology.
- DICOM-native image classification and segmentation models
- Multi-modal imaging AI: CT, MRI, X-ray, pathology, fundus
- FDA SaMD-aligned model development and validation
- Model explainability with attention maps and confidence scores
Predictive Analytics & ML Engineering
Custom machine learning models for clinical risk stratification, operational forecasting, and financial prediction — with full feature engineering and model lifecycle management.
- Tabular ML: gradient boosting, neural networks, and ensemble models
- Time-series forecasting for demand, staffing, and supply chain
- Survival analysis and clinical event prediction
- Automated ML pipelines with drift detection and retraining
Custom AI Development Delivery Process
A structured, sprint-based delivery model with clinical stakeholder validation at every stage.
Problem Framing & Data Assessment
Define the ML problem, assess data availability and quality, and establish success metrics aligned to clinical or business outcomes.
Data Engineering & Feature Development
ETL pipelines, data cleaning, clinical feature engineering, and labelling workflows with domain expert involvement.
Model Development & Iteration
Rapid model prototyping, hyperparameter optimisation, and clinical validation in a sprint-based development cycle.
Clinical Validation & Bias Testing
Prospective and retrospective validation, fairness evaluation across demographic groups, and clinical stakeholder sign-off.
Production Deployment & MLOps
Containerised model deployment, monitoring dashboards, A/B testing infrastructure, and automated retraining pipelines.
Have a Custom AI Problem That Off-the-Shelf Can't Solve?
Tell us about your challenge — our AI engineers will assess feasibility and propose a development approach within 48 hours.