We interviewed Athol Whitten, Founder and CEO of Mezo. He has spent the last eight months in our incubation program after being awarded a place through DataStart, a program in partnership with the Department of Prime Minister & Cabinet.

Tell us a little bit about Mezo.

Mezo implements advanced modelling techniques to help organisations make better decisions and improve their long-term strategies. Right now, we’re working with Victoria Police, Fisheries (Victorian Government) and a number of AFL Clubs. For example, we’re helping Victoria Police determine optimal ways to allocate staffing resources, in order to create an agile and responsive police force and meet future demands.

How did Mezo begin?

Mezo started as a team of two when Simone Stuckey (COO) and I started a small consulting company. Soon after we had a team of three, working as consultants in natural resource management and modelling. Mezo eventually turned itself into a startup company after taking part in the DataStart bootcamp in January.  By the end of the bootcamp, we realised we could be a products and services company with a focus on growing a new kind of product.

What is your flagship product?

Our flagship product is Mezo Flow. It’s an online software-as-a-service platform that allows organisations to connect their data, model their systems, and make better decisions. It also lets users peek into the future, by simulation-testing their management strategies in real time. Our backend technology is up and running, we’ve got models in place and working right now. Our frontend is in prototyping stage, and will be ready for client testing in the new year. Mezo Flow is designed from the ground up to be fast and easy to use.

What is Mezo’s big vision?

Mezo’s big vision is to be a global platform for modelling and decision making. Mezo Flow will enable organisations to connect their own data to other large data sets (such as open government data from the Bureau of Statistics or the Bureau of Meteorology),  and access a suite of models to build a dynamic picture of their world. All sorts of domain-specific organisations could use Mezo Flow. We’re currently working in three major domains: workforce planning, natural resources, and sports, with development plans for agriculture, finance, and corporate systems. We envision a future with many more domains covered, and a steady stream of models and data sources that update and remain at the cutting-edge through time.

How has your experience in the Pollenizer incubator impacted your journey?

Face-to-face coaching has been invaluable. Having Pollenizer coaches on-hand means we deliberately revisit exercises to build our massive vision, and work on our unique value proposition, on a regular basis. It also keeps us in the “lean startup” mindset of being prepared to stay agile and think big.

Before our incubation, we thought we could be a successful small- to medium-sized consulting company.  One month in, we started to develop a different vision. Now we’re very much aiming to be a global company.

What are you working on now?

We’re currently working with a number government organisations, but also with sports teams and in the corporate sector. Right now we’re working with 3-4 models, but we could grow this to hundreds, by creating a generic framework that can work for other domains. We’re designing the Mezo Flow platform to be agile and flexible, so that eventually our models and analytical approach can be used for planning and decision-making at all levels.

How did you expand from working in natural resources to working with sports teams, governments, and corporates?

Michael Smith (Chief Technology Officer at Mezo) and I completed our PhDs in systems modelling, with a focus on marine resource management. We started to branch out and worked on modelling sports teams as a side project. After this we realised we had generic skills that could go further. Then we had an opportunity to build a modelling system for Victoria Police – before we knew it we were working on three different systems. And now we’re looking for our next modelling challenge!

What was one of the biggest learnings you’ve had that you can share with the rest of us?

We’ve learned to continually revisit and rethink our massive goals. We constantly think about the really big picture, and how we want to be part of it. Importantly though, we think about it in small steps: that is, what can we do today that will help us get towards the massive goal?