AI can save money and time. Or it can generate a $20,000 bill overnight. What makes the difference?

Mandip Kanjiya has shipped AI-powered products in HR, insurance, and identity verification. Some worked. Some didn’t. Each one taught clear lessons for leaders who want AI to help, not hurt. In this conversation, Mandip shows why an “advisory layer” helps teams avoid leaking sensitive data, how on-device processing cuts costs and compliance exposure, and where using proven models beats trying to build your own.

Daniel and Mandip get specific: claims triage that moved faster once the workflow was redesigned, HR document generation that stayed private by templating inputs, and KYC checks that extracted only the fields needed on the user’s device. They also talk about where things went wrong and got expensive, including the real-world example of a cloud bill that spiked to $20K in a day and a routing project that failed because bad data looked clean.

🔑 What You’ll Learn in This Episode

• ✅ How to design an AI “advisory layer” that protects company and client data
• ✅ When edge processing reduces latency, spend, and compliance risk
• ✅ The difference between building AI and using AI to build solutions
• ✅ Why enabling policies beat fear-based restrictions for employee adoption
• ✅ How data quality and validation guard against confident, wrong outputs

🔗 Resources & Links

• 🌐 Communitech: https://www.communitech.ca/
• 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

💬 Thinking about bills? Share this with a colleague who signs the cloud bills and start a conversation about your edge strategy.