Today’s customers expect more than great products — they expect seamless, personalised experiences that blend the best of digital and physical. From retail to hospitality to gaming, leading organisations are rethinking how they use data to understand and delight every visitor who walks through the door.

In our recent webinar with Cisco Meraki, we explored how physical environments can now be managed with the same precision and insight as digital ones.

What if you could:

• Capture and analyse customer or vehicle journeys from entry to conversion — just like you would on a website
• Optimise staffing and operations in real time to reduce queues and boost satisfaction
• Detect and prevent loss or security risks, without ever compromising individual privacy

Our panel shared real-world stories of how data and AI are transforming physical experiences, driving measurable business outcomes, operational efficiency, and innovation, while still putting data privacy and security at heart.

Watch the full discussion below to see how organisations are harnessing analytics and ethical AI to create smarter, safer, and more personalised experiences.

Featuring:

• Joy Chua - EVP of Strategy & Development, meldCX
• James McKee - Regional Sales Manager, Cisco Meraki
• Disha Moses - Global Business Develoment Manager, Cisco Meraki

Transcript:

[00:00:00] Joy Chua: Cool. So again, huge shout to Disha and the team for inviting us. My name's Joy, based in Melbourne, Australia. I look after strategy for the group globally. And yeah, just really excited to be here. If there's any questions, like Disha said, feel free to drop us a note or you can leave a comment in the chat box as well.

[00:00:17] Joy Chua: And we'll try our best to get to it as well. Essentially, these were some of the results that we got from one of the playbooks that we released with our joint partners. I split this slide into a couple of things. One is business priorities. And just bear in mind, this was released in early 2025.

[00:00:36] Joy Chua: So that means that probably now, with some of the things that have been happening—and for those of you in Australia, you've seen a lot more information by the Australian Privacy Commission as well—those business priorities may have shifted for 2026. But essentially, at the start of 2025, these are some of the key business priorities.

[00:00:55] Joy Chua: When we did a survey for CIOs and digital leaders last year, we found—so I mentioned this before when Disha was asking me—number one was still about improving employee productivity.

[00:01:14] Joy Chua: So essentially with that, Disha mentioned as well, there's a lot of AI elements when we talk about AI. Does Gen AI, the LLMs, fit into that as agentic AI? And we belong to the little branch that's basically machine vision and some machine learning as well.

[00:01:32] Joy Chua: Essentially, improving employee productivity was top of mind for a lot of operational efficiencies. That could be internal, but it could also be front of house. So it's all about making sure that for a physical space like retail, QSR, or hospitality, for the time a staff member is there, they're performing at their optimum and we can deliver the best service possible. And that was how productivity was measured.

[00:01:50] Joy Chua: We'll talk a little bit about this in particular in some of the slides later when we talk about use cases and how our customers are using it.

[00:02:00] Joy Chua: And then the other big jump that we saw—and I'm pretty sure that if digital leaders were surveyed literally now, this would be number one—was improving regulatory compliance, which has also jumped up quite a bit from 2023 or from 2024.

[00:02:11] Joy Chua: That's continuing to be a key topic. For all of us who are in this AI and machine vision space, I'll share a little bit about how, as meldCX as a company, we've really adhered to making sure that we're sticking to some of these governance frameworks and policies. And you'll see that in some of the slides after.

[00:02:30] Joy Chua: I've also got on the right a little bit about what organizations are seeking help with. And I think this is a key thing for our partners out there. I know there are a couple of partners who are firmly embedded in the Meraki ecosystem.

[00:02:46] Joy Chua: So I think this is for all of us to have a look at in terms of: these are the business priorities for sure—that’s what they’re focused on. What they're seeking help with is what they're looking externally for their partners for, to be able to provide when it comes to their digital infrastructure or data strategy.

[00:03:04] Joy Chua: You can see they're looking externally for data management—better ways to manage data, better ways to ensure that the data they're receiving is stored and referenced the right way. So it's all about making sense of that data.

[00:03:28] Joy Chua: And then of course, making sure that you're reaching out externally for AI knowledge and expertise, especially around scaling—but scaling in a way that helps them stay in line with some of their security and privacy policies. And then number four, making sure that these scalable AI solutions also help deliver measurable business outcomes.

[00:03:44] Joy Chua: So it's a really hands-on approach that a lot of these leaders are looking for as well.

[00:04:00] Joy Chua: So basically, the key summary that we got from that white paper—and something that we've been talking about a lot—was that the future of where things are, especially in AI, belongs to those who can turn data into action or insights without compromising privacy or trust.

[00:04:09] Joy Chua: So a lot of it would be taking what you can see within the boundaries of good governance frameworks and turning that into action. And we’ll unpack that in the slides as to how we do that.

About meldCX

[00:04:31] Joy Chua: So, why meldCX? For those of you who don't know us, meldCX stands for melding the Customer Experience. We're an Australian company headquartered out of Victoria, Melbourne. And like Disha said, we've been a long-time Cisco Meraki ecosystem partner.

[00:04:56] Joy Chua: We're passionate about making sure that not only is infrastructure scalable, it's also easy to deploy. We make it as frictionless and seamless as possible for customers to tap into their data—making sure the data is right and falls within the policy frameworks out there.

[00:05:17] Joy Chua: A key differentiator for us is that we can work with existing infrastructure. We don’t need a GPU for what we do. We work with small format devices. We can run our models either on the camera itself—on the Meraki camera—or on a small format CPU.

[00:05:34] Joy Chua: That reduces upfront costs and increases flexibility for our customers. We can also run hybrid models—some on cameras, others on small form-factor CPUs like an Intel NUC, for example.

[00:05:51] Joy Chua: Customers can reuse much of what they already have. It’s easy, like Disha said, to spin up a POC with one of our off-the-shelf modules and quickly see ROI.

[00:06:00] Joy Chua: We have extensive documentation and videos on how to deploy our installer onto Meraki cameras. You don’t have to be a data scientist or data engineer to roll out an install package—it’s all available for off-the-shelf modules.

Advanced Data Management

[00:06:12] Joy Chua: Another big thing for us is advanced data management. We’re able to ingest data not only from Meraki cameras but also from other sources.

[00:06:39] Joy Chua: One key source is POS transactional data. We work with retail customers to ingest that data, visualize it on dashboards, or export it as CSVs for BI platforms.

[00:07:02] Joy Chua: We also integrate with rostering platforms, especially in hospitality and QSR, to link employee productivity insights.

[00:07:26] Joy Chua: This gives customers a better understanding of engagement and conversion funnels. We can also segregate data across business units when needed, making access secure and organized.

[00:07:42] Joy Chua: We also offer “data playbacks,” deeper-level data insights that unpack anomalies seen in dashboards. We can turn these into marketing packs to show insights such as zone mapping or regional performance comparisons.

[00:08:18] Joy Chua: These services are useful for customers without a data insights team or those seeking independent audits of their results.

Privacy and Governance

[00:08:30] Joy Chua: Last but not least, we work within robust governance frameworks to support AI growth. Everything we do is privacy-first and designed around privacy-driven architecture.

[00:09:00] Joy Chua: So all in all, we’re an AI platform and service that helps businesses drive growth through data. That’s who we are.

[00:09:18] Joy Chua: I think it’s really important to set the scene or framework for what we do. All of us are inundated by AI—training machines, training models, making sure data is ethical.

[00:09:45] Joy Chua: We’ve put together a five-step process for building an ethical AI ecosystem.

[00:10:00] Joy Chua: We have experience working with top global customers and passing numerous risk and compliance assessments. We’ve stood up to unions, risk officers, and compliance teams—all due to our ethical AI methodology.

[00:10:19] Joy Chua: How you train your model is key. We use synthetic data to train models, ensuring they’re free from racial or gender bias. We focus on object and behavior detection (e.g., slip-and-fall, loss prevention).

[00:10:55] Joy Chua: Our models are trained synthetically to simulate business cases like loss prevention, then tested “in the wild.”

[00:11:11] Joy Chua: We don’t store or stream video. There’s real-time de-identification and anonymization—Joy is not “Joy,” she’s “ID002” with certain attributes. These attributes are grouped into broader personas.

[00:11:49] Joy Chua: If edge devices are needed, inferencing happens on-device, encrypted and secure. Only metadata is transmitted to the cloud—no video is stored or processed.

[00:12:03] Joy Chua: Our portal uses tiered user permissions and audit logs to track activity—who moved trip lines, who accessed data, etc. We also offer single sign-on, and MFA is on our roadmap.

[00:12:42] Joy Chua: Here’s how we de-identify people when they enter a camera’s field of view—no personal data is stored. Everyone is treated as an object token with basic attributes.

[00:13:04] Joy Chua: We’re independently certified by a third-party organization in California for compliance with global standards, including the Australian Privacy Act and OECD governance policies.

AI Use Cases

[00:13:31] Joy Chua: We have a playbook showing how we assist customers with machine vision and AI data needs. We’ve made AI palatable by organizing use cases across verticals.

[00:13:53] Joy Chua: Customers can choose modules like people counting, zone engagement, or product shelf engagement. For front-of-house signage, there are modules in the advertising bucket.

[00:14:40] Joy Chua: More complex use cases include theft detection or slip-and-fall monitoring. Some are completely off-the-shelf; others need light customization.

[00:15:00] Joy Chua: Many customers start with one module—say, space analytics—and then expand to safety or advertising modules for a holistic vision analytics suite.

Customer Case Studies

[00:15:36] Joy Chua: One of my favorite parts—some of our use cases and customer findings.

[00:15:55] Joy Chua: This case study is from a multinational quick-service restaurant (QSR) we serve across multiple regions. They wanted to understand ROI from new drive-throughs—how vehicles moved, KPI performance, and opportunities to improve.

[00:16:40] Joy Chua: This evolved from drive-through metrics to in-store operations. We now help them understand staff operations, queue times, and space utilization.

[00:17:17] Joy Chua: We defined key KPIs and validated optimal food and beverage prep processes. After implementation:

• 72% of customers were served within a four-minute target window (order to pickup).
• 21% increase in order preparation speed.
• 40% surge in staff activity during holiday campaigns.

[00:18:39] Joy Chua: These improvements led to higher satisfaction, better engagement, and more efficient resource use across food, POS, and beverage stations.

[00:18:59] Joy Chua: Commercially, that translated to around 300–400 additional meals per store and approximately USD $2 billion in annual revenue growth across locations.

[00:19:19] Joy Chua: They also reduced wastage through predictive ordering—anticipating demand patterns, rescheduling staff, and adjusting inventory accordingly.

Sports Retail Case Study

[00:20:01] Joy Chua: Another use case is Burnley FC, an English Premier League club. We helped map and measure their retail store traffic before, during, and after games.

[00:20:40] Joy Chua: They used these insights to optimize jersey sales, pickup processes, and gender-based merchandising. We’ve got a great quote from Mark, who heads their retail and e-commerce, about how our insights improved fan engagement.

Wrap-Up

[00:20:54] Disha Moses: Thank you, Joy.

[00:20:55] Joy Chua: Yeah. Oh, sorry—we probably have to wrap up shortly. Thank you!

[00:21:00] Joy Chua: Anyway, please make sure to reach out to us. We’ve got a couple of Vision Labs across the world as well. Email us, or reach out to Disha and the team. Thank you, guys.