Blissway Innovation & Technology Culture

What new technologies or frameworks are helping your teams move faster and build smarter?

Honestly, our biggest speed advantage isn’t a specific tool (not even agentic AI), it’s culture. We have a brilliant, yet low-ego team willing to learn and adapt. If someone finds a better way to model the data or handle networking, we don’t cling to what was, we iterate and rebuild. 

We’re often exploring new options like low-light image sensors, or newer segmentation models and embeddings for our computer vision pipeline. Experimentation is built into the calendar itself with periodic "cool-down weeks" where engineers can explore something outside their usual tasks. That open mindset, backed by the budget to experiment and the room to fail along the way, lets our lean team punch above its weight.

How do you balance experimentation with reliability in your development process?

We’re big believers in strong backstops. We strive to get to milestones we trust and have heavily tested and once that foundation is solid, it gives us room to take real risks in the next iteration. If a risk doesn’t pay off, we have a safe landing to fall back to. If it succeeds, it becomes the new backstop and we push again from there.

This matters a lot for us because reliability isn’t optional. We build systems tied to roadside toll infrastructure and when something breaks, it could mean lost toll revenue or billing the wrong vehicle. So there’s a constant tension: what ships to customers has to be rock solid, but the work happening between milestones needs room to be messy, fast and occasionally wrong. Finding that line isn’t a problem we solve once, it’s a constant give and take with every iteration.

What role does collaboration play in turning innovative ideas into products? 

Almost every meaningful product idea here starts as a breakroom theory and ends up crossing three or four disciplines before it ships. A good recent example: one of our electrical engineers, while thinking about the challenges of automatically reading plates, revisited a classic signal-processing concept, the matched filter. It detects a known signal shape buried in noise by correlating the incoming signal against a template of what you expect to find. The better the incoming signal matches the template, the stronger the response.

He realized a license plate’s character string could be treated the same way. Each character has a known shape and the "noise" is everything working against it (shadows, dirt, camera angle or image quality). If you correlate the image against predicted character templates, you get a much stronger signal for what the plate actually says, even when the raw image is messy. He worked out the theory, then partnered with a machine learning engineer to build a prototype, then brought in the software engineers who own our plate-identification tools and got input from a data scientist to help evaluate its impact.