Why NDAH-E
We combine scientific rigor, engineering execution, and healthcare domain expertise to deliver AI systems that perform in production.
Science-Backed Delivery
PhD-level statistical and bioinformatics depth for robust model design, evaluation, and validation.
Engineering to Production
From architecture and APIs to deployment and monitoring, we build reliable AI systems end to end.
Domain Expertise
Specialized delivery across healthcare and life sciences, including clinical text, evidence, and omics workflows.
Facts
Happy Clients
Projects
Hours Of Support
Skills
Technology Stack
Production-ready tools and platforms we use to design, deploy, and scale AI systems.
Services
We deliver end-to-end AI engineering services, from strategy and architecture to deployment, monitoring, and optimization.
AI Engineering, LLM Systems & Applied Machine Learning
Production-grade machine learning and AI systems across NLP, computer vision, and graph AI, designed for reliability and reuse.
Data and AI Engineering
Build scalable data and feature pipelines that power training, inference, monitoring, and continuous model improvement.
Decision Intelligence
Turn fragmented data into decision-ready intelligence through multi-source integration, enrichment, and advanced analytics.
AI Productization, Analytics Applications & Decision Intelligence
Align business goals with AI delivery through clear roadmaps, rapid prototypes, and visualization layers that drive adoption.
Cloud Architecture for AI Workloads
Design cost-efficient cloud architectures and MLOps pipelines for secure deployment, observability, and lifecycle management.
AI Bioinformatics
Deliver standard and custom AI-powered bioinformatics solutions for high-throughput and multi-omics data analysis.
AI Engineering Service Packages
Choose a package aligned to your current stage, from assessment to full production delivery.
AI Readiness Assessment
- Data and use-case audit
- Infrastructure and security review
- ROI and delivery roadmap
Outcome: A prioritized AI roadmap with clear business value and technical feasibility.
LLM Pilot in 4-6 Weeks
- Prototype with retrieval and guardrails
- Task-focused workflow and UX
- Evaluation baseline for quality and risk
Outcome: A working pilot that proves value fast and de-risks production investment.
Production AI System Build
- API and service architecture
- Cloud deployment and MLOps setup
- Monitoring, runbooks, and team handover
Outcome: A production-ready AI system with operational controls and ownership transition.
Healthcare AI Accelerator
- Clinical text and evidence pipelines
- Real-world data and analytics workflows
- Omics and bioinformatics AI integration
Outcome: Faster delivery of domain-specific healthcare AI use cases with trusted outputs.
Technologies We Work With
We use a modern, production-ready AI stack focused on reliability, speed, and measurable outcomes.
Core Engineering
Python, TypeScript, FastAPI, and LangGraph/orchestration frameworks for robust AI services.
Model Integration
OpenAI and open-weight model integration, vector databases, and retrieval pipelines for AI applications.
Platform and Infra
Docker, Kubernetes, AWS, and GCP for secure, scalable deployment and platform operations.
Workflows and MLOps
Airflow workflow orchestration, MLflow experiment tracking, and CI/CD with GitHub Actions.
Data and Analytics
Postgres and BigQuery for transactional and analytical workloads powering model development.
Monitoring and Observability
End-to-end monitoring, tracing, quality evaluation, and reliability controls for production AI systems.
Selected AI Projects
Representative delivery snapshots (anonymized) showing architecture, deployment context, and measurable outcomes.
Clinical Document Intelligence
Problem: Manual review of clinical documents slowed downstream reporting and quality checks.
Solution: Built an NLP pipeline for entity extraction and document classification.
Architecture / stack: Python, FastAPI, Docker, AWS, PostgreSQL, vector search.
Outcome: Reduced manual review time by 60% and improved consistency of coding decisions.
RAG Assistant for Internal Knowledge
Problem: Teams could not quickly find validated answers across SOPs and research documents.
Solution: Delivered a secure retrieval-based assistant with grounded responses and feedback capture.
Architecture / stack: Embeddings, vector database, orchestration layer, observability, feedback loop.
Outcome: Faster access to trusted internal knowledge and reduced repeat SME requests.
Forecasting and Decision Intelligence Platform
Problem: Planning teams relied on disconnected spreadsheets and had limited forecast confidence.
Solution: Built a forecasting service with scenario simulation and dashboarded decision support.
Architecture / stack: Python, XGBoost, Airflow, Docker, GCP, BI dashboards.
Outcome: Improved forecast accuracy by 22% and shortened monthly planning cycles.
Agentic Workflow for Data Operations
Problem: High-volume data QA and triage tasks were repetitive and hard to scale reliably.
Solution: Engineered a multi-agent workflow with human-in-the-loop approvals for exceptions.
Architecture / stack: LLM agents, workflow orchestration, policy guardrails, Kubernetes, monitoring.
Outcome: Cut triage turnaround by 45% while maintaining auditability and operational controls.
Blog
AI engineering thought leadership focused on real-world delivery, risk, and operations.
How to Move from AI Prototype to Production
A practical roadmap from proof-of-concept to stable architecture, deployment, and ownership.
RAG vs Fine-Tuning for Enterprise Knowledge Systems
Trade-offs in cost, latency, governance, and maintainability for enterprise assistants.
AI Evaluation in Healthcare Use Cases
Designing evaluation pipelines that balance clinical reliability, safety, and model performance.
Common Failure Modes in LLM Applications
Patterns behind hallucinations, retrieval misses, and agent breakdowns, with mitigation strategies.
Building Secure GenAI Systems for Regulated Environments
Security controls, governance boundaries, and compliance-ready architecture for sensitive domains.
MLOps and LLMOps Best Practices for Small Teams
Lean operating models for shipping reliable AI quickly with limited headcount and budget.
Let's design your AI system
Tell us your use case and constraints, and we will map the fastest path from concept to production.
Contact
Location:
Mechelen, Belgium
Email:
info@ndaheanalytics.com
Call:
(+32) 0469 24 29 27