The team at Very reviewed Hop and found ways to improve it. After a few incredible modifications such as a module that controls eight taps as compared to the previous one that controlled two, newer facial recognition technology that was absent from the original system, remote operability, and the ability to track the amount of beer poured, Hop is now precisely what Connelly originally envisioned it to be. Very went above and beyond to understand his vision and helped make it a reality. As in the case of Hop, Very provides expert IoT design and development services to clients in industrials and consumer devices world.
In this interview, Veronica Goudzward, marketing manager of Very talks about the team behind their innovative projects, their approach in delivering machine learning services to IoT systems, and their vision for the future.
Please provide us a brief overview of Very.
We are an IoT development firm that focuses both on consumer and industrial IoT, where we use machine learning in different ways for both of those sectors. Hop powered by facial recognition is an example of a device on the consumer IoT sector. In the industrial space, our clients use IoT technology to manufacture equipment and leverage machine learning capabilities to take predictive and preventative measures for maintaining them. With our assistance, they can produce enhanced equipment, and they can sell that at a higher price point and value than other manufacturing companies. We don’t outsource our work; we work from the US and Columbia, which gives us the advantage of completing projects quickly. We have been a remote-first company since day one, and it’s helped us attract and retain some of the most talented designers, developers, data scientists, and project managers from across the country.
Because we have inherently designed our company as a remote workplace, we’re able to give clients a seamless working experience, even though we’re not onsite
What are the challenges that your clients are facing right now that your Machine Learning services can solve?
Manufacturing clients have a lot of data that they could be gathering from the equipment that they are using, such as its performance and effective usage in the field. The need for actionable data is very high as our clients are risk-averse and require projects to be carried out on-time and on-budget. However, most of them cannot access this information, which is a critical challenge. Very provides such critical data by adding sensors and other tracking technologies. We enable them to work proactively by leveraging machine learning. We offer predictive analytics to clients to help them understand the state of equipment— to imply whether the machine is going to fail when it could happen, and so on. Our team of data scientists focuses on helping clients to architect the data that will help them improve their business processes.
What approach or methodology do you adopt while delivering your solutions?
Every client engagement is a highly collaborative partnership. In the beginning, our sales team will do a discovery call and see what the client’s budget is. We need to know if the client has a real need. After the initial stage, we get completely involved with them, and we work fast. We do have consistent check-ins with our clients to make sure we’re always working on the most important things. We use video conferencing to have face-to-face interactions with each other and with our clients and do a “strategic sprint.”
Very’s staff also puts together a tight agenda in advance and typically work a couple of full days together. Our product managers are also available to come onsite for additional strategy sprints on an as-needed basis. During the sprint, we work to define the business needs, the end users, and the solution for those users. Our team identifies the situations in which they will use the product and its features, and then determine their motivations and goals.
The next working session is about getting to a finer level of granularity and talking about specific features. Once we have an outline of the problems and the desired outcome, our engineers get to the testing phase with the client, which can be carried out remotely as well. Throughout our client engagement, we are keen to maintain transparency. We constantly communicate with them and ensure that they know the ins and outs of the entire project lifecycle. Since most of our clients are risk-averse, we do our best to identify potential bottlenecks and roadblocks as early as possible. We do so by analyzing the data collected from the sensors and tracking technologies in the manufacturing equipment, which helps our clients gain insights into any vulnerabilities or equipment malfunctions.
Having established Very in the Consumer and Industrial IoT space, what does the future hold for your organization?
We want to continue to expand in the manufacturing space by scripting several success stories that will exemplify our capabilities in IoT and machine learning technologies. We recently hired a data scientist lead, who is focusing on our machine learning practice, making it more robust than before to serve our clients better. We envision to enhance multiple disciplines within the manufacturing space that can be improved with the prowess of IoT and machine learning, to ultimately drive our clients’ productivity and diversify their revenue streams.