I've been working in the tech industry since 2006. Over more than two decades I worked across some of the most technically advanced environments in the world; Cisco, Verizon, NBC Universal, Glencore. Within these enterprise settings I always sat on the edge of technology, pushing automation and new ways of working across the businesses. Building technology solutions that integrated across multiple business systems and functions to centralise data and bridge gaps between previously segmented worlds.
Since 2020 I have been applying those principles to the business world. Through Ruche Technologies (co-founded 2020) we brought machine learning to sectors that had barely begun to explore what AI could do for them.
Since the beginning of 2026 Flow Dynamics is the bet I've made on that experience. The premise is simple: most operations are sitting on six or seven figures of avoidable cost, buried in the gap between systems that don't talk to each other. The data already exists. What's missing is the intelligence to surface it, and a way to do that without handing sensitive operational data to a cloud vendor.
The thing that turned our heads was transport. We ran our first transport engagement and surfaced £1.3M of avoidable annual cost in 30 days. That result was striking enough that we focused the entire business on UK transport operations from that point on.
The experience behind it
The analytical approach (extracting decisions from operational data, building models that surface what's hidden, doing it inside complex enterprise environments) is what I've done for twenty years. At Cisco and Glencore, that meant building decision-extraction systems across segregated business functions, turning operational data into models that informed commercial decisions.
That work sits on a foundation of deep infrastructure expertise. Enterprise architecture across multi-cloud (AWS, Azure, Google Cloud), virtualisation, unified communications, and full-stack development. CCIE for fourteen years. An MBA with distinction (Beta Gamma Sigma) for the commercial side.
Before Flow Dynamics focused on transport, the same method was applied across other sectors: financial services and insurance among them. Those engagements are referenced on the site. What changed with transport wasn't the method. It was the scale of what the method found.
What makes us different
"Your hardware. Your data. Your network. Proven before you commit."
Most AI vendors want your operational data in their cloud. We do the opposite. We deploy AI hardware to your site, run the models on your own data inside your own network, and nothing leaves your environment. For sensitive operational data, that isn't a feature. It's the whole point.
We're focused only on transport. Not a generalist AI shop dabbling in logistics. A specialist who understands the operation. And you work directly with the person doing the work, not a junior consultant handed the engagement after the sale.
We always start small: a slice of your data, used to prove the savings are real, before you commit to anything bigger. You find out exactly what's possible before making a decision. Not on faith, on evidence.