Quantum Metric, Inc. Innovation & Technology Culture

How do your teams stay ahead of emerging technologies or frameworks?

We treat AI adoption as a learning exercise, not a finish line. Early on, we pushed for broad usage across the company. Not because we had it all mapped out but because real insights only show up once something becomes part of day-to-day work.

What we look for isn’t “what’s the newest tool,” it’s “what changes when this is used at scale?” Where does it reduce friction? Where does trust break down? Where do bottlenecks move? We extended our engineering metrics to track AI usage and impact alongside traditional delivery measures so we’re not relying on intuition alone. We want to know what’s actually working, not just what feels productive.

That internal operating model has directly shaped our product direction. What we’ve learned internally now informs Felix AI agentic, the evolution of AI within our platform, where the focus is on redefining experiences for our customers, not bolting on standalone features.

Can you share a recent example of an innovative project or tech adoption?

As AI adoption increased, we measured usage across different groups and saw cycle time improve overall. But our heaviest AI users showed a subtle increase in cycle time. We started calling it the “power user paradox.”

The lesson wasn’t that AI made people slower. It exposed a new constraint. With AI, we saw more pull requests and often larger ones but review capacity didn’t scale at the same rate. The bottleneck shifted from writing code to reviewing and coordinating changes.

That discovery reframed how we are thinking about AI in engineering. Instead of focusing only on generating code faster, we’ve been experimenting with AI in the later stages of delivery: assisted code review, suggested fixes, build failure triage, flaky test detection and keeping documentation current. Those are the areas that determine whether speed turns into shipped value.

How does your culture support experimentation and learning?

Experimentation and practicing what we preach have always been foundational to how we work at Quantum Metric and AI just gave us a new surface to apply that mindset. Leadership framed it as an opportunity, not a mandate and we intentionally avoided standardizing too early. That gave people room to try tools, share what worked and be honest about what didn’t.

We also make learning easy to spread. We have dedicated channels where people share practical use cases plus regular sessions where teams demo what they’ve tried. The goal is simple: turn one person’s experiment into something other teams can build on whether they’re in engineering, product or operations.

What ties it together is using AI in our own workflows, not just evaluating it in a vacuum. That tight feedback loop — what builds trust, what reduces noise, what genuinely helps people move faster — has become a major input into what we build for customers.

Tell us about a recent product your team launched. How does it drive Quantum Metric’s mission forward?

Recently, we launched Felix AI, a session summarization product that helps consumers focus on what matters and make key decisions faster with one-click quantification. At Quantum Metric, we help companies build cultures maniacally focused on winning the hearts of their customers. With Felix AI, we envision a world where agents know the context before the customer calls, development teams empathize with customers without spending hours watching replays and customer feedback has session context automatically baked in. Being able to quickly and intelligently make the most customer impact is key to winning and keeping loyal customers.

What obstacles and challenges did your team encounter — and overcome — while launching Quantum Metric?

Generative AI and large language models are still very new to the market, so there’s a healthy awareness about security and data usage. We knew this would come up immediately when we introduced anything in this space, and we needed to have a product that would meet the needs of the market. 

Felix AI is built on Google Cloud Platform’s Gemini Pro model, and each customer has its own “instance” of a model, so it’s only trained using their specific dataset. Brands using Felix AI remain confident that their data is never shared to train models outside of their use. Approaching the concerns this way allowed us to accelerate development and maintain the high privacy and data security standards of the entire Quantum Metric platform.

What’s the biggest lesson your team has taken away from this launch, and how has it changed the way your team operates?

I think one of the biggest things I’ve taken away from the Felix AI launch is how a single product can change how the entire organization thinks about customer problems and solutions. Rather than tackling problems with traditional methods, we’re now questioning whether our original approach to some solutions make sense and whether we’re asking the right questions to begin with. This has pushed us to approach problems differently, and we’re now iterating faster in new solution areas as well.