Why AI Works in a Demo but Struggles With Your Data | CUBIG on Lights On Data

Explore Syntitan, CUBIG's AI-Ready Data Platform: https://cubig.ai/syntitan/?utm_source... Your data team says the data is AI-ready. Ready for what? Ho Bae, co-founder and CEO of CUBIG, joins George Firican on the Lights On Data Show to talk about why the same dataset can work for one AI job and fail for another, and what it takes to prepare data for a specific task. They walk through real examples, from service case follow-up decisions to churn prediction and a food supply quoting project. Along the way, they cover how Syntitan fills missing cases with DTS, swaps sensitive values for context-preserving stand-ins with LLM Capsule before they reach AI, and checks whether the prepared data actually helps the task. Originally published on Lights On Data, hosted by George Firican. Watch the original episode:    • Why AI Works in a Demo but Struggles With ...   Lights On Data channel:    / lightsondata   00:00 Welcome 00:51 Why AI Still Struggles 02:22 What AI Ready Means 04:33 Origin of Syntitan 06:53 Agents Versus Data Prep 07:40 Where Syntitan Fits 09:02 Inside the Platform 11:31 Service Case Walkthrough 14:51 Filling Missing Examples 16:16 Why Naive Synthetic Fails 18:12 When Data Exists But Fails 18:27 Context-Preserving Substitution 19:31 Limits of DTS 20:25 Task Based Data Evaluation 21:38 Approval Criteria Over Metrics 23:04 Rescuing And Preventing Failures 24:30 Common Preparation Workflow 25:55 Skip Or Use Diagnostics 26:52 Build Versus Buy Debate 28:30 Reuse What Still Applies 29:27 Buying Guidance And ROI 30:54 Avoiding Consulting Traps 31:47 Non Specialist Friendly UX 32:44 What To Bring To Syntitan 33:44 How To Get In Touch 34:33 Closing Advice And Takeaway #CUBIG #AIReadyData #DataQuality #EnterpriseAI