DISQO Innovation & Technology Culture

How do engineers influence innovation and product strategy?

At DISQO, engineers don’t wait for a finished spec. They sit down with the people who own a problem to understand the real challenge and the job to be done.

When asked to optimize a step, our engineers often ask whether the step should exist at all. With AI changing how problems get solved, we try not to be bound by how things worked in the past.

For example, one of our key brand metrics measures whether people remember a brand without being prompted. It required manual setup for every campaign. Our engineers prototyped an AI-driven approach that needed no extra inputs at all, ran it across dozens of real campaigns and showed it was more accurate than the existing method. It’s now being built into our self-service platform, where simple setup matters most.

By rapidly turning ideas like this into working prototypes, our engineers can prove a strategic concept in hours. A demo makes the case faster than a slide deck. Early on, nothing is off the table technically; guardrails come later, when we review the prototype and technical design. Open exploration first, disciplined evaluation second. That’s how engineers shape strategy, not just execute it.
 

How do you decide when to adopt new tech or tools?

DISQO has an open culture when it comes to new tools and technology. Anyone can bring one forward. The bar isn’t whether something is new or exciting, but whether it solves a real pain point, makes a process meaningfully faster, or replaces a process entirely.

The person proposing a tool owns the first step: building a proof of concept that shows it working against a real problem. They then bring it to Bar Raisers, a weekly meeting open to all of engineering and product, where anyone can present something they’ve found, built, or are thinking about and get candid feedback. If the group comes away confident, the tool moves to a deeper evaluation.

This keeps the barrier to experimentation low, so good ideas can come from anywhere, while grounding adoption decisions in evidence rather than hype. Just as important, Bar Raisers break down silos, so every evaluation becomes a chance for the whole organization to learn, grow and experiment together.
 

What practices help engineers balance experimentation and reliability?

At DISQO, we don’t see experimentation and reliability as a tradeoff. Strong reliability practices are what let us experiment with confidence. Quality extends from our code to everything we put in front of clients, so we integrate it from the start.

That starts with how we ship. New work goes out behind feature flags and rolls out progressively, so we learn from real usage before anything reaches everyone. SLOs and error budgets turn reliability into a shared, measurable target, so teams know when there’s room to push and when to invest in stability. And blameless postmortems mean every lesson makes the next experiment safer.

Nowhere does this balance matter more than with AI. We want to try new approaches as soon as they’re useful, but because of the dynamic nature of these systems, traditional deterministic tests aren’t enough. We have built robust, custom evaluation frameworks into our definition of done to programmatically verify that all outputs are factual, traceable to verified data sources and highly reliable. In production, we continuously monitor performance to ensure consistency. That’s what lets us move fast with emerging tech without compromising trust.