How Tugger Turns Scattered Business Systems Into Trusted AI Answers

Interested in being a guest? Email us at [email protected] AI assistant is only as smart as the mess behind your dashboards. When business data lives across CRM, accounting, HR, and job systems, “connect ChatGPT to our data” quickly turns into rate limits, broken joins, confusing IDs, and answers nobody trusts.We sit down with Craig Morrall, co-founder of Tugger, to unpack a practical architecture for enterprise AI that actually holds up in the real world: pulling data from many platforms into a warehouse, then layering on a semantic model that explains what the data means and how records connect across systems. That extra context is what turns a chatbot into something you can rely on for revenue questions, profitability analysis, and cross-platform reporting without spending months on custom pipelines.Craig also shares what customers are doing once the foundation is in place, including building interactive dashboards in minutes and generating repeatable board packs that used to take finance teams hours. We dig into time to value, early ROI stories, and how Tugger approaches security and governance with ring-fenced data storage, ISO 27001 certification, and guidance on using business-grade LLM plans to reduce training risk.If you’re evaluating enterprise AI, data warehousing, semantic layers, or secure analytics with Claude or ChatGPT, this conversation will help you separate real capability from hype. Subscribe for more practical AI stories, share this with a friend building on enterprise data, and leave a review with the biggest data problem you want AI to solve.Everyday AI: Your daily guide to grown with Generative AICan't keep up with AI? We've got you. Everyday AI helps you keep up and get ahead.Listen on: Apple Podcasts   SpotifySupport the showMore at https://linktr.ee/EvanKirstel