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New York is home to the largest, most well-known enterprises in the world. Rev NYC brings together data science and IT leaders at the cutting edge of AI innovation in industries such as financial services, media and entertainment, technology, and the public sector.
Join us for direct conversations, success stories, and lessons learned on moving from AI experiments to production-grade systems with a lens on regulated industries.
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Enterprises have the models, the talent, and the budgets. What's missing is the operational foundation to turn AI investment into measurable business impact. Discover how a full enterprise AI application platform empowers teams to build, govern, and scale AI-powered decisioning systems.
The pressure to move fast without compromising risk, governance, or regulations has never been greater. Hear the executive perspective on what it takes to scale AI responsibly, bridge the gap between innovation and risk appetite, and build the organizational muscle for sustainable, auditable AI that’s trusted at every level.
How does a small data science team drive outsized impact in a global media company? By tying AI to revenue and scaling influence far beyond headcount, all while navigating a major parent company, governance constraints, and the fearful perceptions of AI.
An enterprise mortgage institution built benchmark models to stress-test production models. Hear how they are reducing the time from experiment to deployment while adhering to the regulatory constraints unique to mortgage lending.
Excel is where your analysts, traders, and risk teams already live. See how Gen AI, ML models, and quant workflows can be surfaced directly in spreadsheets—bridging enterprise-grade ML with the tools your org already trusts, and driving AI adoption with zero friction.
Detecting fraud indicators is the easy part - knowing whether they're actually fraud is where most models fall short. Discover lessons learned from building the organizational and technical foundation that makes scalable fraud detection possible.
Vibe coding lowered the barrier; agentic engineering raises the stakes. As AI takes on full dev lifecycles, the challenge isn't autonomy—it's knowing when humans must stay in the loop. Learn to design HITL checkpoints, build governance, and scale toward dynamic virtual engineering teams.
Regulated enterprises don't have a model problem—they have an application problem. See live examples of AI applications driving real business outcomes, not just proof-of-concepts, and learn how enterprises can build and deploy production-ready AI apps in hours, not weeks.
The guardrails built for predictive models were already straining under GenAI—now agentic systems are breaking them entirely. Discover the AI risk blind spots financial institutions can't afford to ignore, and how to move beyond principles toward eliminating real ethical, reputational, and regulatory exposure.
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