Graphwise Talk#2: Symbolic AI: What Enterprise AI Actually Needs to Know
Symbolic AI for enterprise AI has been quietly doing work that most enterprise teams haven’t fully credited: encoding rules, enforcing logic, and making reasoning transparent enough to audit and defend. But inconsistent terminology has made it hard to evaluate: context graphs, semantic layers, knowledge graphs and symbolic AI are useful concepts, but they are inconsistently used.
That’s a problem when you’re trying to make a build decision.
When AI can’t explain its reasoning, enforce policy consistently, or return reliable answers, organizations face real exposure: failed audits, eroded trust and decisions that can’t be defended.
On May 14, we’re bringing together practitioners who work at this intersection every day to cut through the terminology and get specific about what it actually takes to build AI that holds up: technically and organizationally.
Your hosts:
🔷 Amy Hodler, Founder & Executive Director, GraphGeeks.org
🔷 Todd Blaschka, Senior Executive, Graphwise
🔷 Kent Stroker, Pre-sales Engineer, Graphwise (moderator)
Submit your questions while registering. The best part of this conversation will be shaped by the challenges you’re already facing.
What’s on the agenda:
- What problem are we actually solving? The business requirements for “trusted AI”, and the cost when it fails.
- Cutting through the jargon. What matters, what’s noise, and how to evaluate what your organization actually needs.
- Control, accountability, and human oversight. Where the right architecture changes the equation on compliance, governance, and defensibility.