Ask AI podcast host Carolyne Pelletier checked in with Greg Johnston, President of Carl Data Solutions (rebranded as infinitii ai in 2022), the Vancouver-based predictive analytics software company, to ask the question: How is artificial intelligence supporting environmental monitoring in major cities? Below is an edited transcript of their interview. You can also click on the image below to listen to the podcast.
Carolyne: so Greg I'd love to know more about yourself, your background and more about Carl Data Solutions.
Greg: Sure. Yeah. So I've been with Carl since 2015. My background is in a database structures. Statistics mostly in database marketing actually. Carl Data Solutions is based in Vancouver. Our company specializes in AI-driven predictive analytics. We're a publicly traded company and the stock ticker symbol is a CRL on the CSC. And we're also cross-listed on small cap stock exchanges in the U S and Germany.
"Most recently, their AI technology helped to close the company's largest-ever sale to Los Angeles Sanitation Districts, a utility company that serves over 5.6 million people. It also attracted a new strategic partner called K2 Geospatial who are focused in smart city, seaport, airport and other infrastructure utilities across North America and Europe."
Carolyne: great. Thank you so much. And can you tell me more about how Carl Data Solutions was started.
Greg: Sure. We started back in 2015. The the big starting point for us was the purchase of an application or software as a service application called FlowWorks for water treatment utilities. So now we have about 70 different customers across North America dealing with stormwater system utilities and actually really serve as an application that governs areas where there's tens of millions of people.
Carolyne: And I'd love to know more about the name Carl Data Solutions. How did that all come about?
Greg: My favourite story was it has to do with Carl Jung. A theory of the interconnectedness of all things. So we thought that was pretty appropriate for us since we're trying to connect basically everything through the Industrial Internet of Things.
Carolyne: Wonderful Greg. And can you tell me what's new at Carl?
Greg: Sure. We just finished a big Digital Supercluster project. It was sponsored by the Canadian federal government. We had partners including Teck Resources, Microsoft, Living Lakes Canada, University of Victoria and Genome BC all involved in the project that went on for about 24 months.
It was a significant undertaking, a big amount of work that we built and we focused on constructing an environmental monitoring solution and selected a town in Southeastern BC called Nelson and built a water balance model and a bunch of other tools that came out of it.
Carolyne: Great. And can you tell me more about this project? What is it called and what was the goal of this project?
Greg: The platform we built we call it FlowH2O and it's a comprehensive end-to-end environmental data monitoring system. The great part about it is it's incredibly scalable, and now we're building out that technology for new markets and new customers. We can handle a massive amount of information coming in from multiple disparate places and be able to provide real-time streaming analytics that people need to actually monitor and and make predictions on on infrastructure and environmental conditions and events.
Carolyne: Wonderful. And I'd love to hear more about the technology behind you know what type of AI powers this platform?
Greg: Sure. So one of the greatest pieces of technology that came out of the project was a scripting engine that allows you to create new channels of data by using Machine Learning feedback loops and apply it directly to real-time data that's coming into the system. So as the data comes in alongside reported data, it's creating these new calculated channels based on these pretty sophisticated algorithms that can come up with some some great predictions.
And of course the great part about Machine Learning systems is they they get better with age with more data. The get more time and more feedback you provide, the system just gets better and better and better. So it's a pretty powerful feature and it's featured prominently in a lot of the products that we're coming out with now.
Carolyne: Great. And so I'd love to hear more about the platform itself and how the data is presented. How does it help your users kind of make better decisions with these predictions?
Greg: You can access the data from the platform through an API service or one of our applications. And typically people will bring the information into GIS systems or graphing reporting software. You can do lots of different things. It's like a Swiss army knife for real-time streaming data. You can do all sorts of great things with it including it alarming. So if a particular threshold is met based on four or five different variables for a few different sites or something new is discovered based on an algorithm that's running against the data profile, the correct people can be notified.
This is incredibly beneficial. We made it very functional and an easy to integrate to into different systems.
For the full interview with Greg, click here.
Trusted since 2014 to provide environmental monitoring to many of the largest water utilities in the U.S and Canada, Carl Data Solutions has evolved into a leader in AI-driven predictive analytics for industrial and Smart City infrastructure applications that rely on time-series data. The company serves its customers via a trusted partner network that includes engineering and IT services companies like AECOM, Core & Main, Kerr Wood Leidal, K2 Geospatial and CSL Services.
Carl Data Solutions software performs real-time analysis, checks flow monitoring status, sets alarms through a single interface, accepts all types of data from any source and offers predictive and prescriptive analytics. From real-time, historic, wireless, satellite and SCADA data to public data sets including USGS, NOAA and weather forecasts – it doesn’t matter where the data originates – Carl Data Solutions transforms it into actionable information.