Very
The Go-to IoT Engineering and Deployment Experts

As a fully-distributed IoT engineering firm, Very partners with its clients to build systems for smart manufacturing, smart energy & utilities, consumer electronics, and connected wellness.

Jenn Gamble, Ph. D, the new Data Science Practice Lead at Very, shares that when clients approach the company for answers to their “thorniest” questions, what the company brings to the table is a world-class product team ready to deliver end-to-end IoT solutions. Focused on speed, efficiency, and scalability, Very’s product teams de-risk IoT projects. The company’s partnerships are built on mutual trust, agile workflows, and easy communication. This makes it possible to deliver cutting edge tech to meet its customers’ most pressing challenges. Following is a conversation that CIO Applications had with Jenn to understand why she was drawn to Very, and how she’ll leverage her skills to grow Very’s data science practice.

What are the challenges that CIOs face now in the Artificial Intelligence Solution space, and how is Very effectively addressing these issues?

One thing that’s often under-emphasized is the tight-knit interdisciplinary team needed for a company to build its first few AI products. A data scientist rarely does this by themselves. The team needs competencies in machine learning modeling, data pipeline development, back-end/ API development, front-end development, UI and UX, and product management. It must also work closely with subject matter experts and end-users. No one person is skilled in all these areas, so it’s essential that people with the right mix of skills are brought together, and are encouraged to work closely throughout the process.

It’s also essential to have someone who fills the role of “AI Product Manager” (even if that’s not their official title). Like a traditional product manager, their job is to be heavily focused on how the final machine learning ‘product’ will be used: who the end-users are, what their workflows will be, and what decisions they’ll be making with the information provided. There’s also the added complexity of bringing together the business understanding, data understanding, and what’s possible from a machine learning modeling perspective. In the same way that many of the best product managers are former software engineers, I suspect many of the best AI product managers will be former data scientists (although many other paths are also possible). The field is still so young there aren’t many people who have taken that path yet, but we’re going to see the need for this role continue to grow.

At Very, we help fill this gap for businesses through our machine learning/AI services on client projects.

Please shed some light on the Artificial Intelligence Solution that Very delivers.

On each machine learning project, we have to address:

• What data is available?

• What are the types of statements that we want to be able to make in the application? Is it predictions, recommendations, automated decision-making?

• Who are the end-users?

• What are the workflows that we’re hoping to enable based on this data?

• What’s possible from a predictive modeling or machine learning recommendation perspective?

Then, as we build out the end-to-end application, we apply an agile development approach to all aspects of the system, including the data science development work for the data pipelines and machine learning models.

Please cite a case study on how Very has enabled clients to overcome hurdles and attain desired outcomes.

Hop, an IoT Beer Kiosk System Powered by Facial Recognition’s founders, had a simple goal: to share their passion for beer by delivering a unique, convenient experience to fellow beer lovers. But the product they envisioned required complex hardware-software interactions, which can be surprisingly difficult to execute.

At Very, we’ve developed a unique approach to building connected devices: by applying web tools and technology to the hardware world (through Elixir and Nerves), we can iterate quickly and bring other innovations, like machine learning, to IoT hardware.

Knowing we’d take this approach with Hop, we began the project with a technical audit of their existing hardware and software to understand what was working and what needed improvement. We worked closely with Anthony and his team throughout the process, making every hardware and software decision with his short-term and long-term business objectives in mind.

On the software side, we quickly built and integrated facial recognition technology, which was absent in the original system, by using AWS Rekognition. On the hardware side, we identified a range of improvements that would enhance product performance. For example, the previous system’s compute module controlled only two taps. The module we built can control up to eight taps — so with a single unit, we can control four times the number of taps.

As a result, the new Hop system performs exactly the way Anthony envisioned, and—when ABC is ready—it has the potential to scale seamlessly. (Which is key, because Hop has many applications that extend far beyond the Strip.) Ultimately, our unique approach of bringing web development best practices to the hardware world allowed us to deliver quality hardware and software as a package—which, we’ve found, is pretty rare.

What are the strategies employed by Very to thwart the market competition, and what, according to you, sets Very apart from its competitors?

We have three main differentiators. First, we are a one-stop-shop for our clients’ IoT and AI projects. Whereas many firms only specialize in one area, our expert multi-disciplinary teams can take a customer’s project from concept to production quickly, efficiently, and securely. Second, we are U.S.-based for time zone alignment with our clients. Our team is entirely in-house, and we don’t outsource.

And lastly, we have extensive experience, having launched more than 250 products for companies like Vizio, Mozilla, and Interstate Batteries. We use a proven process to navigate complexity and de-risk AI and IoT projects.

What does the future hold for Very? Any footprint expansion plans or platform enhancement strategies that you can shed light upon?

We’re continuing to take on new, exciting projects for clients in the consumer electronics space, like Loxx Boxx and Koller Products.