If you’ve ever sat through a product demo—especially one focused on ad monetization—you know the drill. There’s the walkthrough, the pitch, and then… the real part begins: the Q&A.
At Airflux, we’ve done our share of demos with game studios of all sizes. And while each game is different, the questions we get at the end tend to sound very familiar. Teams want to know how the system works under the hood, how much dev work it takes, and—understandably—whether it’s safe to run live.
So, we pulled together a list of the most common questions we hear during demos, along with honest, no-fluff answers from our product and data teams. Whether you’re evaluating Airflux or just curious about how AI-powered monetization works in practice, here’s everything game teams ask us—before they decide to try it themselves.
The Questions Game Studios Always Ask About Airflux
We talk to dozens of studios every month—casual, hybrid-casual, hyper-casual. And while each game is different, the questions we hear during Airflux demos tend to be the same. Some are technical, some strategic. Most are driven by one key concern: "Can this really improve monetization without hurting retention—or overloading our team?"
Here’s a breakdown of the questions that come up the most often—and how we typically answer them.
“How is this different from mediation like MAX?”
Mediation solutions like MAX or AppLovin focus on which ad network to serve an impression from. Airflux focuses on whether that impression should happen at all, and when.
In other words, mediation is about supply-side bidding; Airflux is about user-level demand control.
Let’s say the mediation stack preloads ads after every stage. Airflux decides—per user—whether that ad call should actually happen, based on their behavior, predicted churn risk, and monetization potential. For some players, that interstitial triggers after stage 1. For others, not until stage 3. Some users might not see it at all.
“How does it decide which users get more or fewer ads?”
That’s where the AI comes in.
Airflux looks at signals like:
how quickly a player finishes levels
how they react to ads (do they close fast? do they rage quit?)
session depth, return rate, play patterns
IAP probability
time of day, even day of week
It continuously clusters players and runs automated A/B tests to find the right frequency per group—and updates that over time. So the system is always learning, adapting, and finding new pockets of revenue without hurting UX.
“Do we need to tag a lot of in-game events?”
Not really. Our SDK is designed to be lightweight.
We need a handful of events—like session start, ad viewed, level complete, maybe reward claimed—and that’s usually enough. Most teams already track these. If not, we’ll help you plug them in. Integration usually takes a few hours, not days.
“Can we really trust it in a live environment?”
Yes—and we get why you’re asking.
When you’ve spent months tuning a game economy, the last thing you want is to gamble on a new monetization tool.
That’s why we always run Airflux as an experiment first—against a control group. You can see uplift in real time, side-by-side. If results aren’t where they should be, we pause, adjust, or roll it back. You’re always in control.
“What kind of uplift should we expect?”
It depends on the game.
But for a recent hyper-casual title with 10M+ MAU, we saw:
+12% LTV in 3 weeks
+28% after 5 weeks
+54% after 3 months
Some segments showed even higher numbers earlier on. But in general, performance compounds over time, as the system continues to learn.
“Do I need to hire someone to manage this?”
Not at all.
Once integrated, Airflux runs on its own—no LiveOps manager, no backend engineer, no data scientist required. The SDK sends data to the inference API, and the AI handles everything else: optimization, testing, deployment, monitoring.
If you want constraints (e.g. “max 2 interstitials per session”), we can set them up. But you don’t need to define the logic yourself.
## “Does it work with rewarded ads too?”
Yes—and it’s actually one of our strongest areas.
Airflux can personalize reward values to increase engagement without inflating your economy. Think: higher coin rewards for low-engagement users, or smaller-but-frequent rewards for high-retention players. All tuned dynamically.
“What happens when the game changes?”
Airflux retrains automatically.
If you ship a content update, add a new level structure, or shift your ad placement logic, the model will re-learn. It doesn’t require manual intervention. It just needs enough new data to start optimizing again.
“Do we need to use Airbridge to use Airflux?”
Nope. Airflux is built by the Airbridge team, but it’s a standalone product.
You don’t need to use our MMP—or any MMP, for that matter. Airflux works purely off in-game behavior, not attribution data.
Want to talk through your game?
We’re always happy to chat.
If you’re curious how Airflux might work for your specific setup—or want to see a case study from a similar title—[book a quick call](#) and we’ll walk you through it.
We’ve had over 20 game studios reach out after just one LinkedIn post. Maybe you’re next.
