AIAgentsforHealthcare:CareGraph

Business Overview

The client, a US-based healthcare startup, builds technical support for clinicians in the gynecological sector. They saw two things clearly: women's health gets far less attention than it deserves, and clinicians increasingly need fast, data-driven decision support at the point of care.

Their answer was an AI-powered web application that plugs into existing electronic health record systems, with an intelligent chat at its core giving clinicians real-time patient data, curated medical research, and current clinical protocols mid-consultation. To take the idea from research to a real product, they needed extra capacity and deep AI engineering experience in a heavily regulated industry, which is where Xpiderz came in.

Solution

The client arrived with conceptual research and initial ideas. We turned them into a production-ready, HIPAA-compliant application, built from the ground up, deployed on Google Cloud, and integrated with the client's Athena EHR through secure APIs.

The core of the solution is an AI layer between the clinician and the EHR: a set of specialist agents built on LangChain and Google Vertex AI (Gemini), coordinated by an orchestration tool we designed on LangGraph and a Neo4j graph database. That orchestration is what keeps every retrieval and every analysis secure and grounded in factual EHR records.

Outcome

The client received a full application with a dual-layered performance model, built to bridge the gap between traditional record-keeping and predictive analytics:

• Standardized clinical interface. Patient data in a familiar, industry-standard format, organized into clear sections and categories, so clinicians navigate complex medical histories without relearning anything.
• AI-powered inference engine. An analytical layer that proactively spots symptoms across the patient data and highlights them, so the most urgent aspects of care get seen first.