Senzing is the first to deliver real-time AI for entity resolution. Senzing entity resolution
provides organizations with new insights to make better business decisions while reducing
operational costs. The Senzing API is the fastest and easiest way for commercial software
vendors, and organizations in financial services, public sector, information services and other industries to integrate highly accurate, flexible and scalable entity resolution capabilities into applications, data fabrics and other data services. Senzing is Smarter Entity Resolution. 

Senzing

In this live podcast, graph AI expert David Hughes and host Paco Nathan will explore recently emerging practices for leveraging AI tools to help construct knowledge graphs, how these blend with entity resolution. Plus live audience Q\&A.

[BAML](https://docs.boundaryml.com/home) has been the big news story, providing a *domain specific language* (DSL) to generate structured results from LLMs. BAML gets used to implement agentic workflows, validate the consistency of results, data typing, and so on. It works well for extracting entities and relations from unstructured data, which is important for building graphs, and this is where entity resolution enters the scene. We’ll talk through BAML use cases, show a live coding session, and discuss how to leverage features such as *fallbacks* which help with deterministic outcomes, and can also be used to integrate multiple large models and manage costs.

David has also been working on [Multimodal GraphRAG](https://enterprise-knowledge.com/multimodal-graph-rag-mmgraphrag-incorporating-vision-in-search-and-analytics/), incorporating computer vision and audio recognition into search and discovery. For instance, if you have an image of a bench next to a pond, this approach can decompose the image into neighboring trees, people sitting on the beach, and even identify temporal components such as the direction and speed of a bird flying through the scene.

We’ll also discuss leveraging [high dimensionality for associative intelligence](https://home.mlops.community/home/videos/advancing-graphrag-multimodal-integration-w-associative-intelligence-amy-hodler-and-david-hughes-ai-in-production-2025-2025-03-13), and look into the role of *hyperdimensional computing* (HDC) for graph construction and its impact on downstream AI applications. This is very much a cutting-edge topic. In our previous episode with [Mike Dillinger](https://app.getcontrast.io/register/senzing-gphv1e5) we discussed about [*relation resolution*](https://www.linkedin.com/pulse/making-knowledge-graph-relations-first-class-citizens-dillinger-phd-q8rvc/); this area of technology which David is presenting offers good means to improve measures and decisions about the relations in a graph.

[David Hughes](https://nam10.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fdahugh%2F\&data=05%7C02%7Cpaco%40senzing.com%7C583de8702ebb446fb39608dd833dcfef%7Cd83d48aadabe445781d19d2f0a78f963%7C0%7C0%7C638811023690059978%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C\&sdata=Cc0s30mFlVQNGr8UI8KQz70u4tzao7QJV2s3eTEyUi8%3D\&reserved=0) is a Principal Solution Architect at [Enterprise Knowledge](https://enterprise-knowledge.com/) with over a decade of expertise in designing graph-based solutions that reveal transformative insights from complex data. He combines a unique background in clinical practice, medical research, software development, AI, agentic architectures, and cloud engineering to drive impactful solutions. David’s industry experiences span healthcare and biotech, focusing on intensive care, interventional radiology, oncology, cardiology, clinical standards, and proteomics. Outside work, David is an avid endurance runner and hiker, enjoying time with his family and exploring the outdoors.
