What was the scope of their involvement?
We worked closely with InboundMuse to observe their capabilities with the Kraken platform. Over time, we realized they had deep capabilities in AI and also found them very good in terms of visual interface design and data visualization.
Much of what we’ve done with their Kraken platform has been around the data visualization and representation. We’ve used their team to dive down into the granularity of how our solutions need to interface with APIs through bringing data in from other systems.
Our CTO is an accomplished architect with a lot of experience in the AI field, but we didn’t want them to get too involved with the code. We needed to pass off those ideas to like-minded and similarly skilled individuals who could architect down to the next level of detail.
InboundMuse’s team was adept at interpreting high-level architecture instructions and taking them down to the next level of detail in order to comprehend how they would work in reality and in conjunction with other systems.
The interface design phase focused on how we would represent that architecture to the end-user—to hide some of the complexity but still allowing as much control as needed while simultaneously enabling the AI to do its work. Our intention was to make the average user comfortable.
The end product is a self-service data governance and compliance solution. There will be a quite vigorous piece of legislation coming into play in Europe in May 2018 called GDPR (General Data Protection Regulation). It controls companies’ ability to store, manipulate, analyze, and report on personal information, essentially handing back the control of digital information to individuals. A person will be able to request of any company, at any time, what personal data they’re holding and for what purposes.
This is the initial compliance regime that our platform was designed for. Users will be able to interrogate the entire data landscape of an organization based on names, social security numbers, telephone numbers, and other criteria. The platform will then return the personal data held for that individual. The AI component helps parse similar telephone numbers, addresses, different spellings of the same name, and so on.
We haven’t really utilized their team’s inbound and digital marketing skill sets. I know they can provide this, but our main focus has been around AI and blockchain architecture. The scope has been very much on the technology side.
What is the team composition?
We’ve worked with their CTO and CEO on the AI platform’s architecture. We’ve used five more of their members at various times, but I haven’t interacted with them directly.
How did you come to work with InboundMuse?
We’d known them for a long time. Our CTO worked with their CTO and Tyron (CEO, InboundMuse) in previous years, but I first met them in April 2017 when they helped contract our AI solution. We quickly saw that they had interface design capabilities and could address our organization’s marketing needs. They’re familiar with the best ways to represent data.
How much have you invested with them?
We’ve spent around €60,000 ($75,000 USD).
What is the status of this engagement?
We started working with them in April 2017 and have continued on a contract-by-contract basis. The current work was finished in January 2018, but we’re talking to Tyron about the next project which will involve development and integration of a blockchain solution with our current platform.
We’ll release the compliance product in four weeks’ time. It’s a modular platform for which we’re adding different compliance regimes. We’ve completed the GDPR component and are starting work on anti-money-laundering along with other elements.