Podcast Summary: Reshaping Workflows with Dell Pro Precision and NVIDIA RTX GPUs

Episode: Live from GTC – Unlocking Hidden Revenue in Retail with Whale’s Chris May

Host: Logan Lawler (Dell Technologies AI Factory with NVIDIA)
Guest: Chris May, VP of Sales (Whale)
Date: March 19, 2026

Brief Overview

This episode, recorded live at GTC 2026, dives into how AI and visual language models are transforming retail operations for revenue growth. Host Logan Lawler interviews Chris May, VP of Sales at Whale, a technology platform specializing in AI-powered analytics for retail environments. The conversation spotlights real-world applications, integration with existing systems, and the tangible business benefits realized from these advanced workflows.

Key Discussion Points and Insights

1. Introduction to Whale and Its Mission

• Background: Whale has operated for eight years and recently expanded to the US. Their focus is on AI solutions for store operations and revenue optimization.
• Target Audience: Retailers, revenue leaders, and operations managers.

Chris May:

“What we do is basically AI for store operations and revenue growth.” (00:36)

2. How Whale’s Technology Adds Value in Retail

Understanding Revenue-Related Store Data (00:53–01:42)

• Data Points Collected:
  • Customer foot traffic counts
  • Dwell time in store areas
  • Queue length and waiting times
  • Auditing for staff performance (e.g., food/beverage preparation compliance, waste reduction)
• AI Back End: Utilizes vision language models (VLMs), providing analytics for smarter decision-making.

Chris May:

“Are we losing money because people are leaving…spending too much time in line? Are people making burritos or coffee the way that they're supposed to be making?...With our devices and what we call a VLM, visual language model…revenue leaders…can make better decisions.” (00:58–01:41)

Customization and Integration with Retail Systems (01:42–04:08)

• Industry Examples:
  • Luxury Retail (e.g., LVMH): Focus on proper product display, identifying unattended high-value customers.
  • Automotive: Detecting potential car buyers based on time spent near products.

Chris May:

“If I walk into a car dealership and I’m walking around the car for very long, right, why am I not being attended to?...Lifetime customer value could be about half a million dollars.” (02:27–02:40)

• Alert and Analysis Capabilities:
  • Example: Dwell time analysis triggers staff attention to reduce losses (e.g., loitering in beer sections).
  • Core analytics (people tracking, demographic insights, dwell time) are largely ready out of the box, but easily customized.
  • Integrates with stores’ CCTV infrastructure; minimal friction for onboarding.

“The beauty about VLM is you can prompt it to do visual search—and the models are already pretty good…It’s pretty easy to configure that.” (03:32–04:08)

Edge Computing and Data Privacy (04:08–05:18)

• Platform Model: Operates as a Platform-as-a-Service (PaaS).
• Deployment Options:
  • Software layer analyzes video footage.
  • AI edge devices (“AI boxes”) plug into existing CCTV, performing local processing—footage never leaves the store.
  • Only analytics data uploaded to the cloud; dashboards provided for clients.
  • Offers additional “smart” hardware options (cameras, recording badges).

Chris May:

“We have these AI boxes…computing on the edge…The footage never leaves the store and the data gets up…Then we do analytics on the cloud. So you could see a dashboard there. But if you want a smart camera or…a smart recording badge, we also provide that as well.” (04:38–05:18)

3. Notable Quotes & Memorable Moments

• On Platform Flexibility:

  “We kind of are out of the SaaS world. It's more of the PaaS world now—platform as a service.” (04:34)

• On Retail Impact:

  “How many people, or what’s the time that people are not attended to across 2,000 stores?...These are the things that revenue leaders…really care about.” (02:46–03:03)

• On Practical Integration:

  “Typically with any store that has some type of CCTV setup, it's pretty easy to configure that.” (03:59)

• On Domain Names (Ending Light-Hearted Moment):

  “Our website is actually Meatwell AI...I think somebody took the whale AI domain.” (05:25)
  “It would be actually funnier if we did that. But anyways, go meatwhale AI. Check it out.” (05:44)

Timestamps for Important Segments

• 00:36: Chris May introduction & Whale’s mission
• 00:53: Defining AI for store operations
• 01:42: Real-world use cases (luxury retail, automotive), VLM applications
• 03:32: Model customization and integration process
• 04:34: Explanation of platform model and deployment flexibility
• 05:25: Website and social links, humorous closing comments

Conclusion

Chris May offers a window into the future of retail analytics: easy-to-integrate, privacy-conscious AI systems that offer actionable insights at scale. Whale’s approach demonstrates how Dell Pro Precision workstations and NVIDIA RTX GPUs can power these advanced edge and cloud-based workflows—unlocking new revenue streams and operational excellence for retailers worldwide.

For further information or to connect with Chris, visit their website at meatwhale.ai or find Chris May on LinkedIn.