LumiQ is a mobile podcast app where engaging conversations with business leaders count as verifiable CPD for CPAs. With a slick mobile app, automatic CPD tracking, and new episodes weekly, we made CPD something it's never been before: enjoyable.

I helped launch the initial LumiQ MVP, and grow the company to $4 million in ARR, and nearly 50 employees. We maintained YOY growth of over 100%, net negative churn, an NPS score of over 50, and near perfect app store ratings for 3 years.

As the CTO of an early/growth stage startup, my responsibilities were broad, including:

• Software system and application design

• Establishing a devops culture, including trunk-based development, test-driven development, and fully automated CI/CD

• Business and product strategy

• Sprint planning and project management

• Hiring, onboarding and coaching software engineers

Our tech stack included the following:

• React / React-Native

• Firebase

• GraphQL

• Microservice architecture, running in Docker/Kubernetes

Myant is developing a smart clothing platform, which connects the human operating system to the cloud. They call this textile computing.

At Myant, I led all software engineering efforts, ranging from native iOS/Android applications, web based GraphQL API’s, to streaming biometric ingestion engines. I led a team of software engineers distributed between Ottawa and Toronto, and worked closely with UX/UI designers, machine learning specialists, and hardware/firmware engineers.

I also championed an in house biometric processing engine:

• Functional reactive SDK, designed for real-time stream-processing

• Single python process, designed to be run behind a websocket, with minimal latency

• Snapshot and restore capabilities for stateful (i.e. windowed) algorithms

The SDK allowed our machine learning specialists to write machine learning and signal processing algorithms in pure python, while abstracting away the broader system. Data would be collected via sensors embedded in an array of clothing, transmitted via Bluetooth to a mobile app, where it was aggregated and streamed to a cloud environment via a websocket. Algorithm outputs (heart rate, breathing rate, stress, etc.) would be streamed back to the application in real-time. High density data (ex ECG at 1000 Hz) would be serialized, and saved to a custom federated database (Redis, PostgreSQL, S3)

My first foray into the startup world. Built an ambitious prototype, raised $1.9 million in seed capital, and learned a lot about product market fit (or lack thereof).

https://www.indiegogo.com/projects/leo-fitness-intelligence