AI & Analytics
Cresyx AI Lab
CEO & Founder — Cresyx AI Labs
Contact: Jonathan R.
AI development firm specializing in healthcare automation, intelligent integrations, and custom AI-driven solutions — backed by 15+ years of AI, machine learning, and data engineering leadership.
Best for
Healthcare organizations, doctor's offices, and nursing homes seeking custom AI development, rapid prototyping, automation engineering, and intelligent system integrations
Primary outcomes
Custom AI solutions, automated workflows, reduced operational overhead, predictive analytics, and scalable systems aligned with ISO 9001 quality management and healthcare regulations
Timeline & Engagement
Project-based engagements scoped to development complexity, integration requirements, and deployment timeline
What they help with
Jonathan Rapisarda is the founder of Cresyx AI Labs and a seasoned technology leader with over 15 years of experience in artificial intelligence, machine learning, and data engineering. He specializes in rapidly translating business objectives into functional AI prototypes and production-ready systems using modern AI stacks such as retrieval-augmented generation (RAG) systems, agentic workflows, and predictive models. Jonathan can demystify AI for non-technical stakeholders and establish the ISO 9001-aligned processes required for scalable, auditable, and successful production deployment.
Background & Credentials
Cresyx AI Labs is an AI development firm founded by Jonathan Rapisarda — a technologist with over 15 years of experience building intelligent, practical solutions for healthcare and enterprise environments. The firm specializes in custom AI development, automation engineering, and complex technical integrations that enable healthcare organizations to operate at a higher level.
Jonathan brings strategic leadership with an MBA-grounded approach to translating healthcare operational challenges into solvable technology problems. He has architected FastAPI/Flask services integrated with OpenAI and vector databases for semantic search and secure document processing, built gene expression data warehouses across nine studies with star schemas and Python ETL pipelines, and reduced Sev-1 incidents by 70% through standardized ETL patterns in earlier enterprise roles.
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