QED1

QED Focus Areas

Healthcare Technology and Integrations

Healthcare as an industry represents some of the most complex and critical use-cases for automation, whether it is tracking critical patient health records or structuring data for external communications and analysis, Generative AI in concert with other innovative data management solutions are providing new opportunties for improving care and reduce costs. For QED we view this a combination of PHR data integration (FHIR), semantic representation, multi-domain data retrieval and customized workflows that are used to orchestrate and validate these critical Healthcare use-cases. With decades of experience in Healthcare as well as the new and legacy technolgies underpinning current infrastructures we bring the knowledge and infrastructure for solving these challenging problems.






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Graph RAG (Retrival Augmented Generation)

QED has extensive experience in Retrieval Augmented Generation and its even more compelling graph-based RAG systems. Our early work in this technology involved proto-typing and prototyping implementations using LangChain frameworks with Neo4j’s semantic indexing, and later, the Neo4j LLM-graph-builder. This highly extensible platform enables rapid prototyping and provides valuable analytical services, including Chat w/your Document, targeted information extraction, ontology development, and Graph Data Science. These features can be used to enhance highly customized Agentic Workflows across a diverse set of content stores and semantic requirements.

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Azure, GCP & AWS Cloud and Managed Services

Adapting new technology such as Generative AI represents a notable shift from the traditional AI/ML model development and inference deployment. This has fundementally moved the back-end model development to the cloud and the front-end inference to the edge and now leaves companies with a peicemeal set of technology, solution, products and environments to finese for there given use-csaes and environments. Performing this level of solution fit requires subtle set of architectural and operational decisions around Generative AI based managed services versus developing custom Agent Orchestration Frameworks. This is further complicated, as the 3 major hyper-scalers work to extend there Generative AI Eco-system with sometimes struggling service levels. As a result QED continues to analze the Infrastructure and vertical managed service offerings particular in the Healthcare space namely Google Cloud Healthcare API, Azure Health Data Services and AWS HealthLake all of which support the FHIR interoperability standard.





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Neo4j Implementations

Neo4j Graph Implementations

Implementing graph technology isn’t just about setting up a database, it’s about unlocking powerful insights from disparate data sources and ensuring seamless integration with standard ontologies and custom reference data. Whether you’re building your first knowledge graph, scaling to a high-availability cluster, or leveraging Graph Data Science, we provide expert Graph Solution Architecture to help you design, optimize, and deploy your solution effectively.






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Neo4j Graph Implementations

  • • Graph technology models complex data relationships.
  • • Integrates with business use-cases and existing structures.
  • • Shifts from traditional data modeling to graph-based models.
  • • Incorporates industry-standard ontologies: UMLS, SNOMED.
  • • Extends semantic meaning for Graph Data Science and Analytics.
  • • Guides Healthcare use-case development through retrieval queries.
  • • Improves Agentic Workflow with controlled query retrievals.

Healthcare Technology and Integrations

  • • Healthcare industry includes complex, critical automation use-cases.
  • • Tracking patient health records and structuring data.
  • • Generative AI and data management solutions create opportunities.
  • • Improving healthcare, reducing costs through innovative technologies.
  • • PHR data integration (FHIR) for semantic representation.
  • • Multi-domain data retrieval and customized workflows orchestrate use-cases.
  • • Decades of healthcare experience supporting innovative solutions.
  • • Expertise in new and legacy healthcare technologies.

Azure, GCP & AWS Cloud/Managed Services

  • • QED has extensive experience in Retrieval Augmented Generation.
  • • Graph-based RAG systems are even more compelling.
  • • Early work involved proto-typing LangChain implementations with Neo4j.
  • • Neo4j’s semantic indexing and LLM-graph-builder were used.
  • • Highly extensible platform enables rapid prototyping and analytics.
  • • Services include Chat w/your Document, information extraction, ontology.
  • • Graph Data Science enhances customized Agentic Workflows.
  • • Solutions integrate diverse content stores and semantic requirements.

Graph RAG Implementations

  • • Generative AI represents a shift from traditional AI/ML.
  • • Backend model development moves to the cloud.
  • • Frontend inference is shifted to the edge.
  • • Companies face a piecemeal set of technologies.
  • • Solution fit requires architectural and operational decisions.
  • • Generative AI services vs. custom Agent Orchestration frameworks.
  • • Hyper-scalers struggle to extend their Generative AI ecosystems.
  • • QED analyzes managed services in the Healthcare space.
  • • Key services include Google, Azure, and AWS Healthcare APIs.
  • • These services support FHIR interoperability standards.