Retrieval-augmented generation (RAG) is currently the standard architecture to build AI chatbots. But it has one limitation that can lead to potentially disastrous consequences in the enterprise: the inability to provide role-based access control and information security. To make sure that sensitive or restricted information is not accidentally retrieved, it is very important to restrict information from going into a query’s context based on the user’s overall permission and sensitivity of the information. By integrating Realm’s secure connectors with ApertureDB’s graph-vector database engine, we deliver a scalable, real-time access control system ready for enterprise workloads.

Fine-tuning isn't just about throwing more data at a model with the same pretraining loss. That’s just extended pretraining. True fine-tuning means modifying loss functions, adjusting output heads, and optimizing for real-world constraints like confidence calibration, consistency, and latency. This talk explores how misguided fine-tuning practices lead to brittle, inefficient models and demonstrates practical strategies to tailor models to production needs. We dive into when to finetune, the advantages of true finetuning (from output constraints to confidence scores to drastically lower latency) and show how finetuning can be about more than just style.

What should industry know before and during an Agentic AI implementation. What does it actually look like and what are the responsibilities for the consumer. For developers - what is going on in the heads of your customer and how do you coach them through it?

Existing BI and big data solutions primarily consumes structured data, which accounts for only about 20% of enterprise information, leaving vast amounts of unstructured data underutilized. In this talk, we introduce GraphBI, which aims to address this challenge by combining GenAI, graph technology, and visual analytics to unlock the full potential of enterprise data. Technologies like Retrieval-Augmented Generation (RAG) and GraphRAG enhance summarization and Q&A but often function as black boxes, making verification difficult. In contrast, GraphBI takes a different approach: using GenAI for data pre-processing, transforming unstructured data into a graph-based format. This transparent, step-by-step workflow ensures trustworthiness and transparency of the analytics process. In this talk, we’ll walk through the GraphBI workflow, covering best practices and challenges, including: Architectural considerations for projects of varying scales; Data pre-processing, including knowledge map extraction and entity resolution; And Iterative analytics with a BI-focused graph grammar. This approach uniquely surfaces business insights by effectively incorporating all types of data.

Like brilliant but untamed minds, agentic applications in production present a unique challenge: they solve problems in revolutionary ways but can be wildly unpredictable. The art of deploying these free spirits requires a delicate balance between autonomy and reliability. At Orra, we've developed a "glue layer" that acts as a skilled wrangler, ensuring reliability while preserving the agents' freedom in production environments. We'll explore its architecture including our approach to adaptive execution planning, and how we enhance domain understanding.

Whenever you read about taking Retrieval Augmented Generation beyond simple vector search on embeddings, graphs are almost sure to come up. But what graphs? Old-school knowledge graphs, with entities and their relationships, or document-centric graphs, with text snippets as nodes? And how do you use them to improve your retrieval? Nearest neighborhood? PageRank? Something else? I will provide an overview of what's happening in that space, including what I'm doing, and give you a tour of the different options, with their pros and cons.

In a world increasingly saturated with AI-driven applications, businesses face mounting pressure to integrate chatbots into their digital offerings. But is building a chatbot always a good idea? In this talk, we’ll channel our inner Agent Scully—skeptical but willing to investigate—as we guide you through seven critical questions that can help determine whether a chatbot is a wise investment for your company.

Communication is the major bottleneck in large-scale LLM training. In ZeRO++, we quantize both weights and gradients during training in order to reduce the communication volume by 4x, which leads to end-to-end training time reduction by over 50%.