JAY BARTOT, CTO @ MADRONA VENTURE LABS
Please describe your company and your position there.
I am the CTO at a startup studio called Madrona Venture Labs.
For what projects/services did your company hire Xyonix, and what were your goals?
We had a broad product idea that required audio and video processing and natural language processing (NLP) expertise, as well as experience building cloud-based, scalable model training, and inference pipelines. We also needed a team that had product chops and could help connect the product and user experience to the underlying machine learning concepts.
How did you select Xyonix and what were the deciding factors?
I have known Deep for a number of years, having collaborated with him on similar vertical machine learning products. I knew he and his team had the chops to build out this critical side of the product and underlying machine learning technology.
Describe the scope of work in detail, including the project steps, key deliverables, and technologies used.
Like many machine learning-driven products, this project required iterative development, starting simple and basic, adding complexity as more data (esp labeled) is accumulated. More data and examples enabled more features and sophisticated machine learning techniques. A POC was up and going quickly, helping us test the user experience and brainstorm more product features. The pragmatic approach taken by Xyonix helped us manage risk and timelines which enabled us to get something into the user's hands for testing sooner rather than later. I believe the technologies were AWS cloud infrastructure along with open source machine learning and data science tools.
How many people from Xyonix's team worked with you, and what were their positions?
Mainly 2 people from Xyonix, Deep and his associate Bill. Both have strong backgrounds in tech, machine learning and product development.
Can you share any measurable outcomes of the project or general feedback about the deliverables?
What was probably most important in the early stages of product development was the ability to stub in simple versions of the signals identified and extracted by the analytics and machine learning technologies. From there, the application would get better and better each week.
Describe Xyonix’s project management style, including communication tools and timeliness.
Deep and Bill were always well prepared for our meetings, cognizant of the delivery schedule, communicative with us when there would be slippage. They also were forthright on the probabilistic nature of developing solutions to challenging problems. A lot of the work had some experimental or R&D component to it. Deep and Bill never tried to handwave or paint rosy pictures - if they thought something was going to be hard, they were vocal about it.
What did you find most impressive or unique about Xyonix?
They are insightful of the rapidly changing and evolving space of machine learning technology and solutions. Importantly, they are also pragmatic as well as affordable. Their hands-on, practical experience makes them a great choice for startups who are trying to get up and off the ground quickly and helping find product-market-fit. They are also fair, reasonable, and good human beings.
Are there any areas for improvement or something Xyonix could have done differently?
Nothing comes to mind.