Your AI model is smart, fast and well-read, but it knows nothing about your business. How you connect it to your institutional knowledge (e.g., contracts, invoices, emails and records) decides whether you can trust its answers. That’s true whether you’re building the AI, governing the data or relying on the answers to do your job.
Many organizations end up using context stuffing, vector databases, traditional graph databases with GraphRAG or graphs built by LLMs. Each one breaks in predictable places. Passages that look relevant crowd out the real evidence. Sources that conflict go unflagged. Build costs climb with every gigabyte you add.
In this session, we’ll show you why a graph database purpose-built for AI and accelerated by GPUs is your best option. We’ll cover how to: derive ontology from your data; narrow every search based on the context of the question to improve speed and accuracy; and trace each answer to its sources. We’ll also show you how to evaluate databases for AI, including build speed, response time, accuracy and hallucination rate.
In this webinar, you’ll learn:
- Why AI cost, data control and AI-ready data should be urgent priorities for every team that relies on AI;
- Where context stuffing, vector search and GraphRAG break on real business questions;
- Why an ontology derived mathematically from your data reflects the business you have, while a human-defined ontology describes the business you planned;
- Which requirements set a knowledge graph built for AI apart from a database with an AI feature attached;
- How to compare the database options available today; and
- Which questions to ask a database vendor before you commit.
