Fluidly is fundamentally rethinking the way businesses plan and manage cashflow, using data science, accounting domain expertise, and machine learning. While most services focus on what, Fluidly tells you so what, and now what.
I worked with the founders to translate their vision into a product that meets users needs. The app is currently in 'stealth mode', so I can currently show a few images, but not link to the full service.
The service is used by finance professionals within medium sized organisations who currently spend far too much time chasing unpaid invoices. Starting with a Xero integration that analyses a customer's accounts, Fluidly will automatically catalogue and chase invoices. The only time a user needs to get involved is when something goes wrong and needs human intervention.
This relentless focus on only showing what matters saves users time, effectively giving them an extra pair of hands.
The other key feature is the ability to predict the real time it will take to be paid. Every invoice has terms on it — usually something like 30 days — but it seems like almost all end up being paid after that. Fluidly uses machine learning across the ecosystem of users to learn the likely time it will take to actually get paid. Just this alone greatly improves an organisation's ability to manage its cashflow.
After speaking with users, and testing initial prototypes, it became clear that a big part of the day to day job was reporting their organisation's cashflow position to superiors. To address this we created a 'boss board' view that distilled everything a financial controller needed to know, whilst hiding away unnecessary functionality.
The big focus of the board is showing the current, as well as expected funds, and the likey time to get paid. This means executives can stop looking in the rearview mirror, and make decisions around allocating capital more effectively.
I worked as the sole designer on this project, partnering with a business and technical founder. I conducted user research, prototyped designers, and tested then tested those. Insights were quickly distilled into something that could be quickly built and put into the hands of beta testers. I also created a component library and set of design principles to allow the team to continue building out and prototying features.
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