Sunny Ray talks with Evan Burkosky, founder of Kimaru, a decision intelligence platform for supply chain logistics. Evan traces his path from a childhood on his family's commercial fishing boat in Vancouver to building an import business idea that first brought him to Japan in 2003. Over 23 years there, he ran a digital marketing agency, weathered the aftermath of the Fukushima earthquake, and later worked in Japan market entry roles for companies like Optimizely, Dynamic Yield, and Meltwater, learning Silicon Valley go to market playbooks firsthand. Evan explains how those experiences led him to found Kimaru, which applies causal AI and Dr. Lorien Pratt's decision intelligence framework to help supply chain planners navigate tradeoffs around inventory, logistics, tariffs, and disruptions. He compares the system to Google Maps, using real time signals to simulate outcomes and recommend actions that humans can approve or adjust. The conversation moves from fishing boats and Japan market entry to the mechanics of decision intelligence, closing with where listeners can learn more about Kimaru and connect with Evan directly.
A fisherman's son turned Japan market entry expert explains how causal AI can reinvent supply chain decision making.
Can you give a one or two line summary of what Kimaru does?
Kimaru means 'to decide' in Japanese. We're a decision intelligence platform for supply chain logistics. We help people navigate complex decisions that are really a series of tradeoffs by running simulations on different options and then recommending the best action to take.
Tell us about your background. Are you from Canada?
I'm from Vancouver originally and I've spent the last 23 years in Japan. My family grew up in commercial fishing, my father and grandfather included. In the 80s and 90s we sold our catch to Japan, and I had this idea to create a more direct import route for Canadian seafood there, cutting out the middlemen between my dad's boat and the supermarket.
What was it like waking up at dawn to work the deck, and what did those years teach you?
It was the best training to be an entrepreneur. I started at 13 on a crew share, getting a percentage of every fish caught. That was inspiring because everything I did to catch one more fish meant more money. It taught me how everything in a business is integrated, including morale. When everyone was happy, we caught more fish.
What happened when government restrictions made it hard to take over the family boat?
In the 90s the government split the fishing zones into three and brought in licensing and quota restrictions, I think to consolidate fleets. I was also interested in traveling and fascinated by Japan, so I landed there in 2003 and worked with Jetro to explore my seafood import idea, though I hit a lot of protectionism.
How did the 2011 Tohoku earthquake and Fukushima crisis affect your agency, and how did you reinvent yourself?
It devastated inbound tourism, which hit my marketing agency hard since we did a lot of tourism marketing. I struggled for about 18 months with some early angel funding as runway, but eventually had to close the agency and go work for other agencies, which led me into the e agency and later Optimizely, Dynamic Yield, and Meltwater.
What did working with a VC backed company like Optimizely teach you?
It gave me a look inside how a real Silicon Valley startup runs, incredibly structured with intentional sales training. I attended their sales kickoffs in the Bay Area and it was eye opening. Coming from Japan, the casualness shocked me, everyone was just getting things done without worrying about protocol.
Can you explain decision intelligence and how Kimaru applies it to supply chain?
Decision intelligence, as defined by Dr. Lorien Pratt, uses causal decision diagrams to map desired outcomes, actions you control, intermediates, and external factors like tariffs. We sit on top of existing systems of record, pull in external signals like weather and news, and build a digital twin to simulate tradeoffs and recommend the best action for planners to approve or adjust.
How would you describe this system in simpler terms?
It works like Google Maps. You set a destination, or in our case a desired outcome like cost reduction, and the system uses a world model plus real time sensory data to detect disruptions. It has a proprietary metric to judge if a disruption is a small ripple or a bigger tsunami, then recommends an alternate route that the human can accept or override.
Building something daring? Sunny talks to founders like this every day. Fifteen minutes to see if your story belongs on the stage.
Claim your pre-interview